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The document discusses Deep Q-Network (DQN), which combines Q-learning with deep neural networks to allow for function approximation and solving problems with large state/action spaces. DQN uses experience replay and a separate target network to stabilize training. It has led to many successful variants, including Double DQN which reduces overestimation, prioritized experience replay which replays important transitions more frequently, and dueling networks which separate value and advantage estimation.
Reinforcement learning is a machine learning technique that involves trial-and-error learning. The agent learns to map situations to actions by trial interactions with an environment in order to maximize a reward signal. Deep Q-networks use reinforcement learning and deep learning to allow agents to learn complex behaviors directly from high-dimensional sensory inputs like pixels. DQN uses experience replay and target networks to stabilize learning from experiences. DQN has achieved human-level performance on many Atari 2600 games.
Reinforcement Learning (RL) approaches to deal with finding an optimal reward based policy to act in an environment (Charla en Inglés)
However, what has led to their widespread use is its combination with deep neural networks (DNN) i.e., deep reinforcement learning (Deep RL). Recent successes on not only learning to play games but also superseding humans in it and academia-industry research collaborations like for manipulation of objects, locomotion skills, smart grids, etc. have surely demonstrated their case on a wide variety of challenging tasks.
With application spanning across games, robotics, dialogue, healthcare, marketing, energy and many more domains, Deep RL might just be the power that drives the next generation of Artificial Intelligence (AI) agents!
This presentation contains an introduction to reinforcement learning, comparison with others learning ways, introduction to Q-Learning and some applications of reinforcement learning in video games.
Here are the key steps to run a REINFORCE algorithm on the CartPole environment using SLM Lab:
1. Define the REINFORCE agent configuration in a spec file. This specifies things like the algorithm name, hyperparameters, network architecture, optimizer, etc.
2. Define the CartPole environment configuration.
3. Initialize SLM Lab and load the spec file:
```js
const slmLab = require('slm-lab');
slmLab.init();
const spec = require('./reinforce_cartpole.js');
```
4. Create an experiment with the spec:
```js
const experiment = new slmLab.Experiment(spec
This document provides an introduction to deep reinforcement learning. It begins with an overview of reinforcement learning and its key characteristics such as using reward signals rather than supervision and sequential decision making. The document then covers the formulation of reinforcement learning problems using Markov decision processes and the typical components of an RL agent including policies, value functions, and models. It discusses popular RL algorithms like Q-learning, deep Q-networks, and policy gradient methods. The document concludes by outlining some potential applications of deep reinforcement learning and recommending further educational resources.
Deep Reinforcement Learning and Its ApplicationsBill Liu
What is the most exciting AI news in recent years? AlphaGo!
What are key techniques for AlphaGo? Deep learning and reinforcement learning (RL)!
What are application areas for deep RL? A lot! In fact, besides games, deep RL has been making tremendous achievements in diverse areas like recommender systems and robotics.
In this talk, we will introduce deep reinforcement learning, present several applications, and discuss issues and potential solutions for successfully applying deep RL in real life scenarios.
https://www.aicamp.ai/event/eventdetails/W2021042818
The document discusses Deep Q-Network (DQN), which combines Q-learning with deep neural networks to allow for function approximation and solving problems with large state/action spaces. DQN uses experience replay and a separate target network to stabilize training. It has led to many successful variants, including Double DQN which reduces overestimation, prioritized experience replay which replays important transitions more frequently, and dueling networks which separate value and advantage estimation.
Reinforcement learning is a machine learning technique that involves trial-and-error learning. The agent learns to map situations to actions by trial interactions with an environment in order to maximize a reward signal. Deep Q-networks use reinforcement learning and deep learning to allow agents to learn complex behaviors directly from high-dimensional sensory inputs like pixels. DQN uses experience replay and target networks to stabilize learning from experiences. DQN has achieved human-level performance on many Atari 2600 games.
Reinforcement Learning (RL) approaches to deal with finding an optimal reward based policy to act in an environment (Charla en Inglés)
However, what has led to their widespread use is its combination with deep neural networks (DNN) i.e., deep reinforcement learning (Deep RL). Recent successes on not only learning to play games but also superseding humans in it and academia-industry research collaborations like for manipulation of objects, locomotion skills, smart grids, etc. have surely demonstrated their case on a wide variety of challenging tasks.
With application spanning across games, robotics, dialogue, healthcare, marketing, energy and many more domains, Deep RL might just be the power that drives the next generation of Artificial Intelligence (AI) agents!
This presentation contains an introduction to reinforcement learning, comparison with others learning ways, introduction to Q-Learning and some applications of reinforcement learning in video games.
Here are the key steps to run a REINFORCE algorithm on the CartPole environment using SLM Lab:
1. Define the REINFORCE agent configuration in a spec file. This specifies things like the algorithm name, hyperparameters, network architecture, optimizer, etc.
2. Define the CartPole environment configuration.
3. Initialize SLM Lab and load the spec file:
```js
const slmLab = require('slm-lab');
slmLab.init();
const spec = require('./reinforce_cartpole.js');
```
4. Create an experiment with the spec:
```js
const experiment = new slmLab.Experiment(spec
This document provides an introduction to deep reinforcement learning. It begins with an overview of reinforcement learning and its key characteristics such as using reward signals rather than supervision and sequential decision making. The document then covers the formulation of reinforcement learning problems using Markov decision processes and the typical components of an RL agent including policies, value functions, and models. It discusses popular RL algorithms like Q-learning, deep Q-networks, and policy gradient methods. The document concludes by outlining some potential applications of deep reinforcement learning and recommending further educational resources.
Deep Reinforcement Learning and Its ApplicationsBill Liu
What is the most exciting AI news in recent years? AlphaGo!
What are key techniques for AlphaGo? Deep learning and reinforcement learning (RL)!
What are application areas for deep RL? A lot! In fact, besides games, deep RL has been making tremendous achievements in diverse areas like recommender systems and robotics.
In this talk, we will introduce deep reinforcement learning, present several applications, and discuss issues and potential solutions for successfully applying deep RL in real life scenarios.
https://www.aicamp.ai/event/eventdetails/W2021042818
An introduction to reinforcement learningJie-Han Chen
This document provides an introduction and overview of reinforcement learning. It begins with a syllabus that outlines key topics such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal difference learning, deep reinforcement learning, and active research areas. It then defines the key elements of reinforcement learning including policies, reward signals, value functions, and models of the environment. The document discusses the history and applications of reinforcement learning, highlighting seminal works in backgammon, helicopter control, Atari games, Go, and dialogue generation. It concludes by noting challenges in the field and prominent researchers contributing to its advancement.
Dr. Subrat Panda gave an introduction to reinforcement learning. He defined reinforcement learning as dealing with agents that must sense and act upon their environment to receive delayed scalar feedback in the form of rewards. He described key concepts like the Markov decision process framework, value functions, Q-functions, exploration vs exploitation, and extensions like deep reinforcement learning. He listed several real-world applications of reinforcement learning and resources for learning more.
Reinforcement Learning : A Beginners TutorialOmar Enayet
This document provides an overview of reinforcement learning concepts including:
1) It defines the key components of a Markov Decision Process (MDP) including states, actions, transitions, rewards, and discount rate.
2) It describes value functions which estimate the expected return for following a particular policy from each state or state-action pair.
3) It discusses several elementary solution methods for reinforcement learning problems including dynamic programming, Monte Carlo methods, temporal-difference learning, and actor-critic methods.
Deep Reinforcement Learning: Q-LearningKai-Wen Zhao
This slide reviews deep reinforcement learning, specially Q-Learning and its variants. We introduce Bellman operator and approximate it with deep neural network. Last but not least, we review the classical paper: DeepMind Atari Game beats human performance. Also, some tips of stabilizing DQN are included.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
Deep Sarsa and Deep Q-learning use neural networks to estimate state-action values in reinforcement learning problems. Deep Q-learning uses experience replay and a target network to improve stability over the basic Deep Q-learning algorithm. Experience replay stores transitions in a replay buffer, and the target network is periodically updated to reduce bias from bootstrapping. Deepmind's DQN algorithm combined Deep Q-learning with experience replay and a target network to achieve good performance on complex tasks.
In some applications, the output of the system is a sequence of actions. In such a case, a single action is not important
game playing where a single move by itself is not that important.in the case of the agent acts on its environment, it receives some evaluation of its action (reinforcement),
but is not told of which action is the correct one to achieve its goal
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/5u2RiS )
This CloudxLab Reinforcement Learning tutorial helps you to understand Reinforcement Learning in detail. Below are the topics covered in this tutorial:
1) What is Reinforcement?
2) Reinforcement Learning an Introduction
3) Reinforcement Learning Example
4) Learning to Optimize Rewards
5) Policy Search - Brute Force Approach, Genetic Algorithms and Optimization Techniques
6) OpenAI Gym
7) The Credit Assignment Problem
8) Inverse Reinforcement Learning
9) Playing Atari with Deep Reinforcement Learning
10) Policy Gradients
11) Markov Decision Processes
Slides from my presentation of Richard Sutton and Andrew Barto's "Introduction to Reinforcement Learning Chapter 1"
Video (https://www.youtube.com/watch?v=4SLGEq_HZxk&t=2s)
An introduction to reinforcement learning (rl)pauldix
This document provides an introduction to reinforcement learning (RL) and RL for brain-machine interfaces (RL-BMI). It outlines key RL concepts like the environment, value functions, and methods for achieving optimality including dynamic programming, Monte Carlo, and temporal difference methods. It also discusses eligibility traces and provides an example of an online/closed-loop RL-BMI architecture. References for further reading on the topics are included.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
This document discusses how reinforcement learning can be applied in the real world through simplifications and reductions.
Reinforcement learning is challenging to apply to real-world problems due to issues like large state spaces requiring massive amounts of data and delayed rewards. However, it can be simplified by focusing on problems with immediate rewards attributable to single actions and state independence. This reduced problem is known as contextual bandits.
Contextual bandits can be solved using machine learning reductions by treating the agent policy as a classifier and filling in missing reward information to allow supervised learning techniques to be applied. This involves reducing contextual bandits to supervised learning plus an exploration strategy, allowing any classification algorithm to be used as an "oracle learner" for the
In this talk we discuss about the aplicação of Reinforcement Learning to Games. Recently, OpenAI created an algorithm capable of beating a human team in DOTA, considered a game with great amount of complexity and strategy. In this talk, we'll evaluate the role Reinforcement Learning plays in the world of games, taking a look at some of main achievements and how they look like in terms of implementation. We'll also take a look at some of the history of AI applied to games and how things evolved over time.
발표자: 곽동현(서울대 박사과정, 현 NAVER Clova)
강화학습(Reinforcement learning)의 개요 및 최근 Deep learning 기반의 RL 트렌드를 소개합니다.
발표영상:
http://tv.naver.com/v/2024376
https://youtu.be/dw0sHzE1oAc
Reinforcement learning:policy gradient (part 1)Bean Yen
The policy gradient theorem is from "Reinforcement Learning : An Introduction". DPG and DDPG is from the original paper.
original link https://docs.google.com/presentation/d/1I3QqfY6h2Pb0a-KEIbKy6v5NuZtnTMLN16Fl-IuNtUo/edit?usp=sharing
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
The document provides an introduction to reinforcement learning (RL). It discusses the key components of an RL problem, including the environment, reinforcement function, and value function. It also summarizes several popular RL algorithms, including TD(λ), value iteration, Q-learning, and advantage learning. The goal is to present RL at an introductory level for students and researchers across disciplines.
This document introduces machine learning concepts through a webinar presentation. It begins with introductions and definitions of machine learning from Wikipedia and O'Reilly. It then provides examples of artificial intelligence and machine learning applications. The main machine learning concepts covered include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is described as learning from labeled examples, while unsupervised learning finds patterns in unlabeled data. Reinforcement learning involves an agent interacting with an environment and receiving rewards or punishments to achieve goals. Examples of reinforcement learning applications include autonomous vehicles and game playing agents. In closing, the presenter thanks college administrators and attendees for their participation.
An introduction to reinforcement learningJie-Han Chen
This document provides an introduction and overview of reinforcement learning. It begins with a syllabus that outlines key topics such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal difference learning, deep reinforcement learning, and active research areas. It then defines the key elements of reinforcement learning including policies, reward signals, value functions, and models of the environment. The document discusses the history and applications of reinforcement learning, highlighting seminal works in backgammon, helicopter control, Atari games, Go, and dialogue generation. It concludes by noting challenges in the field and prominent researchers contributing to its advancement.
Dr. Subrat Panda gave an introduction to reinforcement learning. He defined reinforcement learning as dealing with agents that must sense and act upon their environment to receive delayed scalar feedback in the form of rewards. He described key concepts like the Markov decision process framework, value functions, Q-functions, exploration vs exploitation, and extensions like deep reinforcement learning. He listed several real-world applications of reinforcement learning and resources for learning more.
Reinforcement Learning : A Beginners TutorialOmar Enayet
This document provides an overview of reinforcement learning concepts including:
1) It defines the key components of a Markov Decision Process (MDP) including states, actions, transitions, rewards, and discount rate.
2) It describes value functions which estimate the expected return for following a particular policy from each state or state-action pair.
3) It discusses several elementary solution methods for reinforcement learning problems including dynamic programming, Monte Carlo methods, temporal-difference learning, and actor-critic methods.
Deep Reinforcement Learning: Q-LearningKai-Wen Zhao
This slide reviews deep reinforcement learning, specially Q-Learning and its variants. We introduce Bellman operator and approximate it with deep neural network. Last but not least, we review the classical paper: DeepMind Atari Game beats human performance. Also, some tips of stabilizing DQN are included.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
Deep Sarsa and Deep Q-learning use neural networks to estimate state-action values in reinforcement learning problems. Deep Q-learning uses experience replay and a target network to improve stability over the basic Deep Q-learning algorithm. Experience replay stores transitions in a replay buffer, and the target network is periodically updated to reduce bias from bootstrapping. Deepmind's DQN algorithm combined Deep Q-learning with experience replay and a target network to achieve good performance on complex tasks.
In some applications, the output of the system is a sequence of actions. In such a case, a single action is not important
game playing where a single move by itself is not that important.in the case of the agent acts on its environment, it receives some evaluation of its action (reinforcement),
but is not told of which action is the correct one to achieve its goal
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/5u2RiS )
This CloudxLab Reinforcement Learning tutorial helps you to understand Reinforcement Learning in detail. Below are the topics covered in this tutorial:
1) What is Reinforcement?
2) Reinforcement Learning an Introduction
3) Reinforcement Learning Example
4) Learning to Optimize Rewards
5) Policy Search - Brute Force Approach, Genetic Algorithms and Optimization Techniques
6) OpenAI Gym
7) The Credit Assignment Problem
8) Inverse Reinforcement Learning
9) Playing Atari with Deep Reinforcement Learning
10) Policy Gradients
11) Markov Decision Processes
Slides from my presentation of Richard Sutton and Andrew Barto's "Introduction to Reinforcement Learning Chapter 1"
Video (https://www.youtube.com/watch?v=4SLGEq_HZxk&t=2s)
An introduction to reinforcement learning (rl)pauldix
This document provides an introduction to reinforcement learning (RL) and RL for brain-machine interfaces (RL-BMI). It outlines key RL concepts like the environment, value functions, and methods for achieving optimality including dynamic programming, Monte Carlo, and temporal difference methods. It also discusses eligibility traces and provides an example of an online/closed-loop RL-BMI architecture. References for further reading on the topics are included.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
1. Reinforcement learning involves an agent learning through trial-and-error interactions with an environment. The agent learns a policy for how to act by maximizing rewards.
2. The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
3. Temporal difference learning combines the advantages of Monte Carlo methods and dynamic programming by allowing for incremental learning through bootstrapping predictions like dynamic programming while also learning directly from experience like Monte Carlo methods.
This document discusses how reinforcement learning can be applied in the real world through simplifications and reductions.
Reinforcement learning is challenging to apply to real-world problems due to issues like large state spaces requiring massive amounts of data and delayed rewards. However, it can be simplified by focusing on problems with immediate rewards attributable to single actions and state independence. This reduced problem is known as contextual bandits.
Contextual bandits can be solved using machine learning reductions by treating the agent policy as a classifier and filling in missing reward information to allow supervised learning techniques to be applied. This involves reducing contextual bandits to supervised learning plus an exploration strategy, allowing any classification algorithm to be used as an "oracle learner" for the
In this talk we discuss about the aplicação of Reinforcement Learning to Games. Recently, OpenAI created an algorithm capable of beating a human team in DOTA, considered a game with great amount of complexity and strategy. In this talk, we'll evaluate the role Reinforcement Learning plays in the world of games, taking a look at some of main achievements and how they look like in terms of implementation. We'll also take a look at some of the history of AI applied to games and how things evolved over time.
발표자: 곽동현(서울대 박사과정, 현 NAVER Clova)
강화학습(Reinforcement learning)의 개요 및 최근 Deep learning 기반의 RL 트렌드를 소개합니다.
발표영상:
http://tv.naver.com/v/2024376
https://youtu.be/dw0sHzE1oAc
Reinforcement learning:policy gradient (part 1)Bean Yen
The policy gradient theorem is from "Reinforcement Learning : An Introduction". DPG and DDPG is from the original paper.
original link https://docs.google.com/presentation/d/1I3QqfY6h2Pb0a-KEIbKy6v5NuZtnTMLN16Fl-IuNtUo/edit?usp=sharing
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
The document provides an introduction to reinforcement learning (RL). It discusses the key components of an RL problem, including the environment, reinforcement function, and value function. It also summarizes several popular RL algorithms, including TD(λ), value iteration, Q-learning, and advantage learning. The goal is to present RL at an introductory level for students and researchers across disciplines.
This document introduces machine learning concepts through a webinar presentation. It begins with introductions and definitions of machine learning from Wikipedia and O'Reilly. It then provides examples of artificial intelligence and machine learning applications. The main machine learning concepts covered include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is described as learning from labeled examples, while unsupervised learning finds patterns in unlabeled data. Reinforcement learning involves an agent interacting with an environment and receiving rewards or punishments to achieve goals. Examples of reinforcement learning applications include autonomous vehicles and game playing agents. In closing, the presenter thanks college administrators and attendees for their participation.
Machine learning interview questions and answerskavinilavuG
Machine learning interview questions and answers are provided. Key points include:
1) Machine learning is a form of AI that automates data analysis to enable computers to learn and adapt through experience without explicit programming.
2) Candidate sampling in machine learning involves calculating probabilities for a random sample of negative labels in addition to all positive labels, to reduce computational costs during training.
3) The difference between data mining and machine learning is that data mining extracts patterns from unstructured data, while machine learning relates to designing algorithms that allow computers to learn without being explicitly programmed.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
This document summarizes an incremental machine learning algorithm applied to robot navigation. The algorithm learns a set of declarative rules by executing random actions and observing the results. The rules are then pruned to remove useless rules. Initially, crisp conditions are used in the rules, but fuzzy conditions learned from a human expert produce better results. The algorithm is demonstrated through a robot simulation navigating an obstacle-free path, first with crisp and then fuzzy rules. The fuzzy rules improve performance by better handling uncertainties close to the goal direction.
This document summarizes an incremental machine learning algorithm applied to robot navigation. The algorithm learns a set of declarative rules by executing random actions and observing the results. The rules are then pruned to remove useless rules. Initially, crisp conditions are used in the rules, but fuzzy conditions learned from a human expert produce better results. The algorithm is demonstrated through a robot simulation navigating an obstacle-free path, first with crisp rules, which work satisfactorily, and then with fuzzy rules, which produce better results.
1) Machine learning involves analyzing data to find patterns and make predictions. It uses mathematics, statistics, and programming.
2) Key aspects of machine learning include understanding the business problem, collecting and preparing data, building and evaluating models, and different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
3) Common machine learning algorithms discussed include linear regression, logistic regression, KNN, K-means clustering, decision trees, and handling issues like missing values, outliers, and feature engineering.
Hibridization of Reinforcement Learning Agentsbutest
The document discusses reinforcement learning techniques for developing intelligent agents that can learn from interactions with their environment. It provides background on reinforcement learning methods like dynamic programming, Monte Carlo methods, and temporal-difference learning. The paper aims to show how hybridizing classic reinforcement learning agents like SARSA and SARSA(λ) through comparative testing can significantly improve their performance.
1. The document discusses different types of machine learning algorithms including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, transduction, and learning to learn.
2. It provides more detail on supervised learning and unsupervised learning. Supervised learning involves using labeled examples to generate a function that maps inputs to outputs, while unsupervised learning models a set of inputs without labeled examples.
3. The supervised learning process involves collecting a dataset, pre-processing the data by handling missing values and outliers, selecting relevant features, and training and evaluating a classifier on training and test sets.
Machine Learning with Python- Methods for Machine Learning.pptxiaeronlineexm
The document discusses various machine learning methods for building models from data including supervised learning methods like classification and regression as well as unsupervised learning methods like clustering and dimensionality reduction. It also covers semi-supervised learning and reinforcement learning. Supervised learning uses labeled training data to learn relationships between inputs and outputs while unsupervised learning discovers patterns in unlabeled data.
Online learning & adaptive game playingSaeid Ghafouri
The document discusses online learning and adaptive game playing. It defines online learning as processing data sequentially in a streaming fashion to train machine learning models. This allows learning from large datasets that cannot fit in memory or when data is continuously generated. Common applications include recommendations, fraud detection, and portfolio management. The document also discusses how reinforcement learning differs from online learning in having a goal of optimizing rewards through a sequence of actions rather than predicting single outputs. It describes early implementations of adaptive game playing using algorithms like naive Bayes, Markov decision processes, and n-grams on the game of rock-paper-scissors before discussing a more complex fighting game implementation.
Rapid Motor Adaptation for Legged Robots (RMA) allows quadruped robots to rapidly adapt their walking gait when faced with new terrains or conditions. RMA consists of a base policy trained via reinforcement learning to walk in simulation, and an adaptation module that estimates environment factors to allow the base policy to adapt in real-time. When deployed on the A1 robot, RMA achieved a high success rate walking over various challenging terrains like sand, mud, and obstacles, without any failures in trials. The adaptation module allows the robot to adapt its gait within fractions of a second to respond to changes in conditions, outperforming alternatives that are slower to adapt or require explicit system identification.
This document provides an overview of machine learning. It defines machine learning as a type of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It discusses supervised, unsupervised, and reinforcement learning algorithms. Some commonly used algorithms are naive Bayes, k-means clustering, support vector machines, Apriori, linear regression, and logistic regression. The document also outlines applications of machine learning such as computer vision, natural language processing, and medical analysis.
The document discusses credit apportionment in rule-based expert systems. It describes a framework that includes a system environment sub-model, principles of usefulness, and definitions of the credit apportionment problem. The credit apportionment problem involves estimating the inherent usefulness of rules from payoffs received. The document also reviews a bucket brigade algorithm approach to credit apportionment that uses context variables and array-valued strengths. Finally, it discusses an expert system called GAMBLE that uses a genetic algorithm and credit apportionment for branch selection advice.
The document discusses different types of agent programs:
- Simple reflex agents select actions based only on the current percept, ignoring percept history. This is implemented with condition-action rules.
- Model-based reflex agents maintain an internal state to track unobserved aspects of the world based on a model of how the world works. This allows handling partial observability.
- Goal-based agents consider goals that describe desirable situations and choose actions to achieve goals based on the current state and results of actions.
- Utility-based agents assign numeric utilities to states and choose actions that maximize expected utility, allowing comparison of states in achieving multiple goals.
Modeling and simulation is the use of models as a basis for simulations to develop data utilized for managerial or technical decision making. In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model.
This document provides an overview of machine learning presented by Mr. Raviraj Solanki. It discusses topics like introduction to machine learning, model preparation, modelling and evaluation. It defines key concepts like algorithms, models, predictor variables, response variables, training data and testing data. It also explains the differences between human learning and machine learning, types of machine learning including supervised learning and unsupervised learning. Supervised learning is further divided into classification and regression problems. Popular algorithms for supervised learning like random forest, decision trees, logistic regression, support vector machines, linear regression, regression trees and more are also mentioned.
How to formulate reinforcement learning in illustrative waysYasutoTamura1
This lecture introduces reinforcement learning and how to approach learning it. It discusses formulating the environment as a Markov decision process and defines important concepts like policy, value functions, returns, and the Bellman equation. The key ideas are that reinforcement learning involves optimizing a policy to maximize expected returns, and value functions are introduced to indirectly evaluate and improve the policy through dynamic programming methods like policy iteration and value iteration. Understanding these fundamental concepts through simple examples is emphasized as the starting point for learning reinforcement learning.
The document discusses automatic problem solving, which involves generating sequences of actions (plans) to achieve desired goals by transforming one world model into another. It describes the basic capabilities needed for automatic problem solving systems, including managing state description models, deductive machinery to reason about states, and action models to describe how the world changes with actions. It also outlines common control strategies for plan generation, such as means-ends analysis and backtracking, and notes tactics can improve the efficiency of these strategies, such as hierarchical planning.
The document proposes a new algorithm to optimize robot configuration for sequential part assembly by multi-robots. It formulates the grasp planning problem as a Constraint Satisfaction Problem (CSP) to find the minimum number of regrasp operations. The algorithm solves smaller CSPs for each operation separately, then incrementally adds transfer constraints to reduce regrasps. Experimental results show the algorithm generates near-optimal solutions faster than brute-force methods. Future work could examine robot cooperation through client-server architectures or swarm intelligence, and extend the CSP approach to additional constraints like limited workspace or additional robot types like fastening robots.
Similar to Reinforcement Learning / E-Book / Part 1 (20)
Virtualization: A Key to Efficient Cloud ComputingHitesh Mohapatra
The document discusses various types of virtualization used in cloud computing. It describes virtualization as a technique that allows sharing of physical resources among multiple customers. There are two main types of hypervisors - Type 1 hypervisors run directly on hardware while Type 2 hypervisors run on a host operating system. The document also summarizes different types of virtualization including hardware, software, memory, storage, network, and desktop virtualization. Benefits of virtualization include improved efficiency, outsourcing of hardware costs, testing software in isolated environments, and emulating machines beyond physical availability.
Automating the Cloud: A Deep Dive into Virtual Machine ProvisioningHitesh Mohapatra
Virtual machine provisioning allows users to quickly provision new virtual machines through a self-service interface in minutes, rather than the days it previously took to provision physical servers. Virtual machine migration also allows live migration of virtual machines between physical hosts in milliseconds for maintenance or upgrades. Standards like OVF and OCCI help ensure interoperability and portability of virtual machines across platforms. The virtual machine lifecycle includes provisioning, serving requests, and deprovisioning resources when the service is ended.
Harnessing the Power of Google Cloud Platform: Strategies and ApplicationsHitesh Mohapatra
The document discusses Google Cloud Platform (GCP), a suite of cloud computing services provided by Google. It provides infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). GCP allows users to access computing power, storage, databases, and other applications through remote servers on the internet. It offers advantages like scalability, security, redundancy, and cost-effectiveness compared to traditional data centers. Example applications of GCP include enabling collaborative document editing in real-time.
Scheduling refers to allocating computing resources like processor time and memory to processes. In cloud computing, scheduling maps jobs to virtual machines. There are two levels of scheduling - at the host level to distribute VMs, and at the VM level to distribute tasks. Common scheduling algorithms include first-come first-served (FCFS), shortest job first (SJF), round robin, and max-min. FCFS prioritizes older jobs but has high wait times. SJF prioritizes shorter jobs but can starve longer ones. Max-min prioritizes longer jobs to optimize resource use. The choice depends on goals like throughput, latency, and fairness.
This document provides a template for submitting case studies to a case study compendium on cloud computing solutions. The template requests information on the customer organization, industry, location, the cloud solution provider, area of application of the cloud solution, challenges addressed, objectives, timeline of implementation, solution approach, challenges during implementation, benefits to the customer, innovation enabled, partnerships involved, and a customer testimonial. It requests details on the cloud solution type (IaaS, PaaS, or SaaS), quantitative and qualitative benefits realized by the customer, and how the solution helped boost innovation. Contact details of the submitter are also requested. The focus is on how cloud platforms and solutions enabled customer enterprises to innovate and
RAID (Redundant Array of Independent Disks) uses multiple hard disks or solid-state drives to protect data by storing it across the drives in a way that if one drive fails, the data can still be accessed from the other drives. There are different RAID levels that provide varying levels of data protection and performance. A RAID controller manages the drives in an array, presenting them as a single logical drive and improving performance and reliability. Common RAID levels include RAID 0 for performance without redundancy, RAID 1 for disk mirroring, and RAID 5 for striping with parity data distributed across drives. [/SUMMARY]
Cloud load balancing distributes workloads and network traffic across computing resources in a cloud environment to improve performance and availability. It routes incoming traffic to multiple servers or other resources while balancing the load. Load balancing in the cloud is typically software-based and offers benefits like scalability, reliability, reduced costs, and flexibility compared to traditional hardware-based load balancing. Common cloud providers like AWS, Google Cloud, and Microsoft Azure offer multiple load balancing options that vary based on needs and network layers.
ITU-T requirement for cloud and cloud deployment modelHitesh Mohapatra
List and explain the functional requirements for networking as per the ITU-T technical report. List and explain cloud deployment models and list relative strengths and weaknesses of the deployment models with neat diagram.
The document contains descriptions of several LeetCode problems ranging from Medium to Hard difficulty. It provides details about the Maximum Level Sum of a Binary Tree, Jump Game III, Minesweeper, Binary Tree Level Order Traversal, Number of Operations to Make Network Connected, Open the Lock, Sliding Puzzle, and Trapping Rain Water II problems. It also includes pseudocode and explanations for solving the Number of Operations to Make Network Connected and Open the Lock problems.
The document discusses three problems: (1) finding the cheapest flight route between two cities with at most k stops using DFS and pruning; (2) merging k sorted linked lists into one sorted list using a priority queue; (3) using a sequence of acceleration (A) and reversing (R) instructions to reach a target position in the shortest number of steps for a car that can move to negative positions.
Trie Data Structure
LINK: https://leetcode.com/tag/trie/
Easy:
1. Longest Word in Dictionary
Medium:
1. Count Substrings That Differ by One Character
2. Replace Words
3. Top K Frequent Words
4. Maximum XOR of Two Numbers in an Array
5. Map Sum Pairs
Hard:
1. Concatenated Words
2. Word Search II
The document discusses the basics of relational databases. It defines what a database is, the advantages it provides over file-based data storage, and some disadvantages. It also covers relational database concepts like tables, records, fields, keys, and normalization. The document explains how to design a relational database by determining the purpose and entities, modeling relationships with E-R diagrams, and following steps to normalize the data.
The document discusses measures of query cost in database management systems. It explains that query cost can be measured by factors like the number of disk accesses, size of the table, and time taken by the CPU. It further breaks down disk access time into components like seek time, rotational latency, and sequential vs. random I/O. The document then provides an example formula to calculate estimated query cost based on these components.
This document discusses how wireless sensor networks (WSNs) can be used in smart city applications. It first defines WSNs as self-configured, infrastructure-less networks that use sensors to monitor conditions like temperature, sound, and pollution. It then discusses how WSNs can influence lifestyle by enabling applications in areas like healthcare, transportation, the environment and more. Finally, it discusses how WSNs are a primary strength for smart cities by allowing remote and cost-effective monitoring of infrastructure and resources across applications like smart water, smart grid, and smart transportation.
The document provides an overview and syllabus for a course on fundamentals of data structures. It covers topics such as linear and non-linear data structures including arrays, stacks, queues, linked lists, trees and graphs. It describes various data types in C like integers, floating-point numbers, characters and enumerated types. It also discusses operations on different data structures and analyzing algorithm complexity.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Height and depth gauge linear metrology.pdfq30122000
Height gauges may also be used to measure the height of an object by using the underside of the scriber as the datum. The datum may be permanently fixed or the height gauge may have provision to adjust the scale, this is done by sliding the scale vertically along the body of the height gauge by turning a fine feed screw at the top of the gauge; then with the scriber set to the same level as the base, the scale can be matched to it. This adjustment allows different scribers or probes to be used, as well as adjusting for any errors in a damaged or resharpened probe.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
2. Reinforcement Learning with MATLAB | 2
What Is Reinforcement Learning?
Reinforcement learning is learning what to do—how to map
situations to actions—so as to maximize a numerical reward
signal. The learner is not told which actions to take, but instead
must discover which actions yield the most reward by trying them.
—Sutton and Barto, Reinforcement Learning: An Introduction
Reinforcement learning (RL) has successfully trained
computer programs to play games at a level higher than the
world’s best human players.
These programs find the best action to take in games with
large state and action spaces, imperfect world information,
and uncertainty around how short-term actions pay off in the
long run.
Engineers face the same types of challenges when designing
controllers for real systems. Can reinforcement learning also
help solve complex control problems like making a robot walk
or driving an autonomous car?
This ebook answers that question by explaining what RL is
in the context of traditional control problems and helps you
understand how to set up and solve the RL problem.
3. Reinforcement Learning with MATLAB | 3
The Goal of Control
Broadly speaking, the goal of a control system is to determine the
correct inputs (actions) into a system that will generate the desired
system behavior.
With feedback control systems, the controller uses state observations
to improve performance and correct for random disturbances and
errors. Engineers use that feedback, along with a model of the
plant and environment, to design the controller to meet the system
requirements.
This concept is simple to put into words, but it can quickly become
difficult to achieve when the system is hard to model, is highly
nonlinear, or has large state and action spaces.
4. Reinforcement Learning with MATLAB | 4
The Control Problem
To understand how complexity complicates a control design problem,
imagine developing a control system for a walking robot.
To control the robot (i.e., the system), you command potentially dozens
of motors that operate each of the joints in the arms and legs.
Each command is an action you can take. The state observations
come from multiple sources, including a camera vision sensor,
accelerometers, gyros, and encoders for each of the motors.
The controller has to satisfy multiple requirements:
• Determine the right combination of motor torques to get the robot
walking and keep it balanced.
• Operate in an environment that has random obstacles that
need to be avoided.
• Reject disturbances like wind gusts.
A control system design would need to handle these as well as any
additional requirements like maintaining balance while walking down
a steep hillside or across a patch of ice.
5. Reinforcement Learning with MATLAB | 5
The Control Solution
Typically, the best way to approach this problem is to break it up into
smaller discrete sections that can be solved independently.
For example, you could build a process that extracts features from
the camera images. These might be things like the location and type
of obstacle, or the location of the robot in a global reference frame.
Combine those states with the processed observations from the other
sensors to complete the full state estimation.
The estimated state and the reference would feed into the controller,
which would likely consist of multiple nested control loops. The outer
loop would be responsible for managing high-level robot behavior (like
maybe maintaining balance), and the inner loops manage low-level
behaviors and individual actuators.
All solved? Not quite.
The loops interact with each other, which makes design and tuning
challenging. Also, determining the best way to structure these loops
and break up the problem is not simple.
6. Reinforcement Learning with MATLAB | 6
The Appeal of Reinforcement Learning
Instead of trying to design each of these components separately,
imagine squeezing everything into a single function that takes in all of
the observations and outputs the low-level actions directly.
This certainly simplifies the block diagram, but what would this function
look like and how would you design it?
It might seem like creating this single large function would be more
difficult than building a control system with piecewise subcomponents;
however, this is where reinforcement learning can help.
7. Reinforcement Learning with MATLAB | 7
Reinforcement Learning: A Subset of Machine Learning
Reinforcement learning is one of three broad categories of machine learning. This ebook does not focus on unsupervised or supervised learning,
but it is worth understanding how reinforcement learning differs from these two.
8. Reinforcement Learning with MATLAB | 8
Machine Learning: Unsupervised Learning
Unsupervised learning is used to find patterns or hidden structures in datasets that have not been categorized or labeled.
For example, say you have information on the physical attributes and social tendencies of 100,000 animals. You could use unsupervised learning
to group the animals or cluster them into similar features. These groups could be based on number of legs, or based on patterns that might not be
as obvious, such as correlations between physical traits and social behavior that you didn’t know about ahead of time.
9. Reinforcement Learning with MATLAB | 9
Machine Learning: Supervised Learning
Using supervised learning, you train the computer to apply a label to a given input. For example, if one of the columns of your dataset of animal
features is the species, you can treat species as the label and the rest of the data as inputs into a mathematical model.
You could use supervised learning to train the model to correctly label each set of animal features in your dataset. The model guesses the
species, and then the machine learning algorithm systematically tweaks the model.
With enough training data to get a reliable model, you could then input the features for a new, unlabeled animal, and the trained model would
apply the most probable species label to it.
10. Reinforcement Learning with MATLAB | 10
Machine Learning: Reinforcement Learning
Reinforcement learning is a different beast altogether. Unlike the other two learning frameworks, which operate using a static dataset, RL works
with data from a dynamic environment. And the goal is not to cluster data or label data, but to find the best sequence of actions that will generate
the optimal outcome. The way reinforcement learning solves this problem is by allowing a piece of software called an agent to explore, interact
with, and learn from the environment.
The agent is able to observe the current state
of the environment.
The environment changes state and produces a reward for
that action. Both of which are received by the agent.
From the observed state, it decides which action to take.
Using this new information, the agent can determine whether that action was
good and should be repeated, or if it was bad and should be avoided.
The observation-action-reward cycle continues until learning is complete.
11. Reinforcement Learning with MATLAB | 11
Anatomy of Reinforcement Learning
Within the agent, there is a function that takes in state observations
(the inputs) and maps them to actions (the outputs). This is the single
function discussed earlier that will take the place of all of the individual
subcomponents of your control system. In the RL nomenclature, this
function is called the policy. Given a set of observations, the policy
decides which action to take.
In the walking robot example, the observations would be the angle of
every joint, the acceleration and angular velocity of the robot trunk, and
the thousands of pixels from the vision sensor. The policy would take
in all of these observations and output the motor commands that will
move the robot’s arms and legs.
The environment would then generate a reward telling the agent how
well the very specific combination of actuator commands did. If the
robot stays upright and continues walking, the reward will be higher
than if the robot falls to the ground.
12. Reinforcement Learning with MATLAB | 12
Learning the Optimal Policy
If you were able to design a perfect policy that would correctly
command the right actuators for every observed state, then your job
would be done.
Of course, that would be difficult to do in most situations. Even if you
did find the perfect policy, the environment might change over time, so
a static mapping would no longer be optimal.
This brings us to the reinforcement learning algorithm.
It changes the policy based on the actions taken, the observations
from the environment, and the amount of reward collected.
The goal of the agent is to use reinforcement learning algorithms to
learn the best policy as it interacts with the environment so that, given
any state, it will always take the most optimal action—the one that will
produce the most reward in the long run.
13. Reinforcement Learning with MATLAB | 13
What Does It Mean to Learn?
To understand what it means for a machine to learn, think
about what a policy actually is: a function made up of logic
and tunable parameters.
Given a sufficient policy structure (logical structure),
there is a set of parameters that will produce an optimal
policy—a mapping of states to actions that produces the
most long-term reward.
Learning is the term given to the process of systematically
adjusting those parameters to converge on the optimal
policy.
In this way, you can focus on setting up an adequate
policy structure without manually tuning the function to get
the right parameters.
You can let the computer learn the parameters on its own
through a process that will be covered later on, but for now
you can think of as fancy trial and error.
14. Reinforcement Learning with MATLAB | 14
How Is Reinforcement Learning Similar to Traditional Controls?
The goal of reinforcement learning is similar to the control
problem; it’s just a different approach and uses different
terms to represent the same concepts.
With both methods, you want to determine the correct
inputs into a system that will generate the desired system
behavior.
You are trying to figure out how to design the policy
(or the controller) that maps the observed state of the
environment (or the plant) to the best actions (the actuator
commands).
The state feedback signal is the observations from the
environment, and the reference signal is built into both the
reward function and the environment observations.
15. Reinforcement Learning with MATLAB | 15
Reinforcement Learning Workflow Overview
In general, five different areas need to be addressed with reinforcement learning. This ebook focuses on the first area, setting up the environment.
Other ebooks in this series will explore reward, policy, training, and deployment in more depth.
You need an environment where your agent can learn. You need to choose what
should exist within the environment and whether it’s a simulation or a physical setup.
You need to choose a way to represent the policy.
Consider how you want to structure the parameters
and logic that make up the decision-making part
of the agent.
You need to think about what you ultimately want your agent to do and
craft a reward function that will incentivize the agent to do just that.
You need to choose an algorithm to train the agent
that works to find the optimal policy parameters.
Finally, you need to exploit the policy by deploying
it in the field and verifying the results.
16. Reinforcement Learning with MATLAB | 16
Environment
The environment is everything that exists outside of the agent. It is where the agent sends actions,
and it is what generates rewards and observations.
This definition can be confusing if you’re coming from a controls perspective
because you may tend to think of the environment as disturbances that impact the
system you’re trying to control.
However, in reinforcement learning nomenclature, the environment is everything
but the agent. This includes the system dynamics. In this way, most of the
system is actually part of the environment. The agent is just the bit of software
that is generating the actions and updating the policy through learning.
17. Reinforcement Learning with MATLAB | 17
Model-Free Reinforcement Learning
One reason reinforcement learning is so powerful is that the agent
does not need to know anything about the environment. It can still
learn how to interact with it. For example, the agent doesn’t need to
know the dynamics or kinematics of the walking robot. It will still figure
out how to collect the most reward without knowing how the joints
move or the lengths of the appendages.
This is called model-free reinforcement learning.
With model-free RL, you can put an RL-equipped agent into any
system and the agent will be able to learn the optimal policy. (This
assumes you’ve given the policy access to the observations, rewards,
actions, and enough internal states.)
18. Reinforcement Learning with MATLAB | 18
Model-Based Reinforcement Learning
Here is the problem with model-free RL. If the agent has no understanding of the environment,
then it must explore all areas of the state space to figure out how to collect the most reward.
This means the agent will need to spend some time exploring low-reward areas during the
learning process.
However, you may know some parts of the state space that are not worth exploring.
By providing a model of the environment, or part of the environment, you provide the
agent with this knowledge.
Using a model, the agent can explore parts of the environment without having to physically take
that action. A model can complement the learning process by avoiding areas that are known to
be bad and exploring the rest.
19. Reinforcement Learning with MATLAB | 19
Model Free vs. Model Based
Model-based reinforcement learning can lower the time it takes to learn
an optimal policy because you can use the model to guide the agent
away from areas of the state space that you know have low rewards.
You don’t want the agent to reach these low-reward states in the first
place, so you don’t need it to spend time learning what the best actions
would be in those states.
With model-based reinforcement learning, you don’t need to know the
full environment model; you can provide the agent with just the parts of
the environment you know.
Model-free reinforcement learning is the more general case and will be
the focus for the rest of this ebook.
If you understand the basics of reinforcement learning without a model,
then continuing on to model-based RL is more intuitive.
Model-free RL is popular right now because people hope to use
it to solve problems where developing a model—even a simple
one—is difficult. An example is controlling a car or a robot from pixel
observations. It’s not intuitively obvious how pixel intensities relate to
car or robot actions in most situations.
20. Reinforcement Learning with MATLAB | 20
Real vs. Simulated Environments
Since the agent learns through interaction with the environment, you need a way for the agent to actually interact with it. This might be a real
physical environment or a simulation, and choosing between the two depends on the situation.
Real
Accuracy: Nothing represents the environment more completely than
the real environment.
Simplicity: There is no need to spend the time creating and validating
a model.
Necessary: It might be necessary to train with the real environment if it
is constantly changing or difficult to model accurately.
Simulated
Speed: Simulations can run faster than real time or be parallelized,
speeding up a slow learning process.
Simulated conditions: It is easier to model situations that would be
difficult to test.
Safety: There is no risk of damage to hardware.
21. Reinforcement Learning with MATLAB | 21
Real vs. Simulated Environments
For example, you could let an agent learn how to balance an inverted
pendulum by running it with a physical pendulum setup. This might be
a good solution since it’s probably hard for the hardware to damage
itself or others. Since the state and action spaces are relatively small, it
probably won’t take too long to train.
With the walking robot this might not be such a good idea. If the policy
is not sufficiently optimal when you start training, the robot is going
to do a lot of falling and flailing before it even learns how to move its
legs, let alone how to walk. Not only could this damage the hardware,
but having to pick the robot up each time would be extremely time
consuming. Not ideal.
22. Reinforcement Learning with MATLAB | 22
Benefits of a Simulated Environment
Simulated environments are the most common way to train an agent. One nice benefit for control problems is that you usually already have a
good model of the system and environment since you typically need it for traditional control design. If you already have a model built in MATLAB®
or Simulink®
, you can replace your existing controller with a reinforcement learning agent, add a reward function to the environment, and start the
learning process.
Learning is a process that requires lots of samples: trials, errors, and
corrections. It is very inefficient in this sense because it can take
thousands or millions of episodes to converge on an optimal solution.
A model of the environment may run faster than real time, and you can
spin up lots of simulations to run in parallel. Both of these approaches
can speed up the learning process.
You have a lot more control over simulating conditions than you do exposing your agent
to them in the real world.
For example, your robot may have to be capable of walking on any number of different surfaces.
Simulating walking on a low-friction surface like ice is much simpler than testing on actual ice.
Additionally, training an agent in a low-friction environment would actually help the robot stay
upright on all surfaces. It’s possible to create a better training environment with simulation.
23. Reinforcement Learning with MATLAB | 23
Reinforcement Learning with MATLAB and Simulink
Reinforcement Learning Toolbox provides functions and blocks for
training policies using reinforcement learning algorithms. You can use
these policies to implement controllers and decision-making algorithms
for complex systems such as robots and autonomous systems.
The toolbox lets you train policies by enabling them to interact with
environments represented in MATLAB or using Simulink models.
For example, for defining reinforcement learning environments in
MATLAB, you can use provided template scripts and classes and
modify the environment dynamics, reward, observations, and actions
as needed depending on the application.
In Simulink, you can model the many different types of environments
that are often needed to solve controls and reinforcement learning
problems. For example, you can model vehicle dynamics and flight
dynamics; a variety of physical systems with Simscape™; dynamics
approximated from measured data with System Identification
Toolbox™; sensors such as radars, lidars, and IMUs; and more.
mathworks.com/products/reinforcement-learning