this talk was an introduction to Reinforcement Learning based on the book by Andrew Barto and Richard S. Sutton. We explained the main components of an RL problem and detailed the tabular solutions and approximate solutions methods.
An efficient use of temporal difference technique in Computer Game LearningPrabhu Kumar
This document summarizes an efficient use of temporal difference techniques in computer game learning. It discusses reinforcement learning and some key concepts including the agent-environment interface, types of reinforcement learning tasks, elements of reinforcement learning like policy, reward functions, and value functions. It also describes algorithms like dynamic programming, policy iteration, value iteration, and temporal difference learning. Finally, it mentions some applications of reinforcement learning in benchmark problems, games, and real-world domains like robotics and control.
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 provides an overview of reinforcement learning and some key algorithms used in artificial intelligence. It introduces reinforcement learning concepts like Markov decision processes, value functions, temporal difference learning methods like Q-learning and SARSA, and policy gradient methods. It also describes deep reinforcement learning techniques like deep Q-networks that combine reinforcement learning with deep neural networks. Deep Q-networks use experience replay and fixed length state representations to allow deep neural networks to approximate the Q-function and learn successful policies from high dimensional input like images.
Reinforcement learning is a computational approach for learning through interaction without an explicit teacher. An agent takes actions in various states and receives rewards, allowing it to learn relationships between situations and optimal actions. The goal is to learn a policy that maximizes long-term rewards by balancing exploitation of current knowledge with exploration of new actions. Methods like Q-learning use value function approximation and experience replay in deep neural networks to scale to complex problems with large state spaces like video games. Temporal difference learning combines the advantages of Monte Carlo and dynamic programming by bootstrapping values from current estimates rather than waiting for full episodes.
Reinforcement learning is a machine learning technique that involves an agent learning how to achieve a goal in an environment by trial-and-error using feedback in the form of rewards and punishments. The agent learns an optimal behavior or policy for achieving the maximum reward. Key elements of reinforcement learning include the agent, environment, states, actions, policy, reward function, and value function. Reinforcement learning problems can be solved using methods like dynamic programming, Monte Carlo methods, and temporal difference learning.
This presentation discusses Markov decision processes (MDPs) for solving sequential decision problems under uncertainty. An MDP is defined by a tuple containing states, actions, transition probabilities, and rewards. The objective is to find an optimal policy that maximizes expected long-term rewards by choosing the best sequence of actions. Value iteration is introduced as an algorithm for computing optimal policies by iteratively updating the value of each state. The presentation also discusses MDP terminology, stationary policies, influence diagrams, and methods for solving large MDP problems incrementally using decision trees.
The document discusses challenges in reinforcement learning. It defines reinforcement learning as combining aspects of supervised and unsupervised learning, using sparse, time-delayed rewards to learn optimal behavior. The two main challenges are the credit assignment problem of determining which actions led to rewards, and balancing exploration of new actions with exploitation of existing knowledge. Q-learning is introduced as a way to estimate state-action values to learn optimal policies, and deep Q-networks are proposed to approximate Q-functions using neural networks for large state spaces. Experience replay and epsilon-greedy exploration are also summarized as techniques to improve deep Q-learning performance and exploration.
This document discusses reinforcement learning, an approach to machine learning where an agent learns behaviors through trial and error interactions with its environment. The agent receives positive or negative feedback based on its actions, allowing it to maximize rewards. Specifically:
1) In reinforcement learning, an agent performs actions in an environment and receives feedback in the form of rewards or punishments to learn behaviors without a teacher directly telling it what to do.
2) The goal is for the agent to learn a policy to map states to actions that will maximize total rewards. It must figure out which of its past actions led to rewards through the "credit assignment problem."
3) Reinforcement learning has been applied to problems like game playing, robot control
An efficient use of temporal difference technique in Computer Game LearningPrabhu Kumar
This document summarizes an efficient use of temporal difference techniques in computer game learning. It discusses reinforcement learning and some key concepts including the agent-environment interface, types of reinforcement learning tasks, elements of reinforcement learning like policy, reward functions, and value functions. It also describes algorithms like dynamic programming, policy iteration, value iteration, and temporal difference learning. Finally, it mentions some applications of reinforcement learning in benchmark problems, games, and real-world domains like robotics and control.
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 provides an overview of reinforcement learning and some key algorithms used in artificial intelligence. It introduces reinforcement learning concepts like Markov decision processes, value functions, temporal difference learning methods like Q-learning and SARSA, and policy gradient methods. It also describes deep reinforcement learning techniques like deep Q-networks that combine reinforcement learning with deep neural networks. Deep Q-networks use experience replay and fixed length state representations to allow deep neural networks to approximate the Q-function and learn successful policies from high dimensional input like images.
Reinforcement learning is a computational approach for learning through interaction without an explicit teacher. An agent takes actions in various states and receives rewards, allowing it to learn relationships between situations and optimal actions. The goal is to learn a policy that maximizes long-term rewards by balancing exploitation of current knowledge with exploration of new actions. Methods like Q-learning use value function approximation and experience replay in deep neural networks to scale to complex problems with large state spaces like video games. Temporal difference learning combines the advantages of Monte Carlo and dynamic programming by bootstrapping values from current estimates rather than waiting for full episodes.
Reinforcement learning is a machine learning technique that involves an agent learning how to achieve a goal in an environment by trial-and-error using feedback in the form of rewards and punishments. The agent learns an optimal behavior or policy for achieving the maximum reward. Key elements of reinforcement learning include the agent, environment, states, actions, policy, reward function, and value function. Reinforcement learning problems can be solved using methods like dynamic programming, Monte Carlo methods, and temporal difference learning.
This presentation discusses Markov decision processes (MDPs) for solving sequential decision problems under uncertainty. An MDP is defined by a tuple containing states, actions, transition probabilities, and rewards. The objective is to find an optimal policy that maximizes expected long-term rewards by choosing the best sequence of actions. Value iteration is introduced as an algorithm for computing optimal policies by iteratively updating the value of each state. The presentation also discusses MDP terminology, stationary policies, influence diagrams, and methods for solving large MDP problems incrementally using decision trees.
The document discusses challenges in reinforcement learning. It defines reinforcement learning as combining aspects of supervised and unsupervised learning, using sparse, time-delayed rewards to learn optimal behavior. The two main challenges are the credit assignment problem of determining which actions led to rewards, and balancing exploration of new actions with exploitation of existing knowledge. Q-learning is introduced as a way to estimate state-action values to learn optimal policies, and deep Q-networks are proposed to approximate Q-functions using neural networks for large state spaces. Experience replay and epsilon-greedy exploration are also summarized as techniques to improve deep Q-learning performance and exploration.
This document discusses reinforcement learning, an approach to machine learning where an agent learns behaviors through trial and error interactions with its environment. The agent receives positive or negative feedback based on its actions, allowing it to maximize rewards. Specifically:
1) In reinforcement learning, an agent performs actions in an environment and receives feedback in the form of rewards or punishments to learn behaviors without a teacher directly telling it what to do.
2) The goal is for the agent to learn a policy to map states to actions that will maximize total rewards. It must figure out which of its past actions led to rewards through the "credit assignment problem."
3) Reinforcement learning has been applied to problems like game playing, robot control
Deep reinforcement learning from scratchJie-Han Chen
1. The document provides an overview of deep reinforcement learning and the Deep Q-Network algorithm. It defines the key concepts of Markov Decision Processes including states, actions, rewards, and policies.
2. The Deep Q-Network uses a deep neural network as a function approximator to estimate the optimal action-value function. It employs experience replay and a separate target network to stabilize learning.
3. Experiments applying DQN to the Atari 2600 game Space Invaders are discussed, comparing different loss functions and optimizers. The standard DQN configuration with MSE loss and RMSProp performed best.
Reinforcement learning is a machine learning technique where an agent learns how to behave in an environment by receiving rewards or punishments for its actions. The goal of the agent is to learn an optimal policy that maximizes long-term rewards. Reinforcement learning can be applied to problems like game playing, robot control, scheduling, and economic modeling. The reinforcement learning process involves an agent interacting with an environment to learn through trial-and-error using state, action, reward, and policy. Common algorithms include Q-learning which uses a Q-table to learn the optimal action-selection policy.
This document is a final report for a CS799 course that explores using reinforcement learning to train an agent to play a chasing game. The author defines the game environment and mechanics, then uses Q-learning with an epsilon-greedy exploration strategy to train an agent to maximize its score by collecting vegetables while avoiding walls, minerals, and other players. The agent is trained in multiple phases to first avoid walls, then minerals, and finally other players while collecting vegetables. Results are presented comparing training with different exploration vs exploitation settings.
This document provides an overview of reinforcement learning concepts. It introduces reinforcement learning as using rewards to learn how to maximize utility. It describes Markov decision processes (MDPs) as the framework for modeling reinforcement learning problems, including states, actions, transitions, and rewards. It discusses solving MDPs by finding optimal policies using value iteration or policy iteration algorithms based on the Bellman equations. The goal is to learn optimal state values or action values through interaction rather than relying on a known model of the environment.
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
Matineh Shaker, Artificial Intelligence Scientist, Bonsai at MLconf SF 2017MLconf
This document discusses deep reinforcement learning and concept network reinforcement learning. It begins with an introduction to reinforcement learning concepts like Markov decision processes and value-based methods. It then describes Concept-Network Reinforcement Learning which decomposes complex tasks into high-level concepts or actions. This allows composing existing solutions to sub-problems without retraining. The document provides examples of using concept networks for lunar lander and robot pick-and-place tasks. It concludes by discussing how concept networks can improve sample efficiency, especially for sparse reward problems.
The document is a seminar report submitted by Kalaissiram S. for their Bachelor of Technology degree. It discusses reinforcement learning (RL), including the key concepts of agents, environments, actions, states, rewards, and policies. It also covers the Bellman equation, types of RL, Markov decision processes, popular RL algorithms like Q-learning and SARSA, and applications of RL.
This document provides an introduction to reinforcement learning. It defines reinforcement learning and compares it to machine learning. Key concepts in reinforcement learning are discussed such as policy, reward function, value function and environment. Examples of reinforcement learning applications include chess, robotics, petroleum refineries. Model-free and model-based methods are introduced. The document also discusses Monte Carlo methods, temporal difference learning, and Dyna-Q architecture. Finally, it provides examples of reinforcement learning problems like elevator dispatching and job shop scheduling.
This document provides an introduction to reinforcement learning. It defines reinforcement learning and compares it to supervised learning. Reinforcement learning involves an agent interacting with an environment and receiving rewards to learn a policy for maximizing rewards. The key elements of reinforcement learning problems are the agent, environment, state, actions, policy, reward function, and value function. The document discusses various reinforcement learning concepts like exploration vs exploitation, temporal difference learning, Q-learning, and Monte Carlo methods. It also compares model-based and model-free reinforcement learning approaches. Overall, the document provides a high-level overview of the main concepts and problem-solving methods in the field of reinforcement learning.
Andrii Prysiazhnyk: Why the amazon sellers are buiyng the RTX 3080: Dynamic p...Lviv Startup Club
Andrii Prysiazhnyk: Why the amazon sellers are buiyng the RTX 3080: Dynamic pricing with RL
AI & BigData Online Day 2021
Website - http://aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
The Reinforcement Learning (RL) is a particular type of learning. It is useful when we try to learn from an unknown environment. Which means, that our model will have to explore the environment in order to collect the necessary data to use for its training. The model is represented as an Agent, trying to achieve a certain goal in a particular environment. The Agent affects this environment by taking actions that change the state of the environment and generate rewards produced by this later one.
The learning relies on the generated rewards, and the goal will be to maximize them. To choose the actions to apply, the agents use a policy. It can be defined as the process that the agent use to choose the actions that will permit it to optimize the overall rewards. In this course, we will see two methods used to develop these polices: policy gradient and Q-Learning. We will implement our examples using the following libraries: OpenAI gym, keras , tensorflow and keras-rl.
[Notebook 1](https://colab.research.google.com/drive/1395LU6jWULFogfErI8CIYpi35Y00YiRj)
[Notebook 2](https://colab.research.google.com/drive/1MpDS5rj-PwzzLIZtAGYnZ_jjEwhWZEdC)
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Discrete sequential prediction of continuous actions for deep RLJie-Han Chen
This paper proposes a method called SDQN (Sequential Deep Q-Network) to solve continuous action problems using a value-based reinforcement learning approach. SDQN discretizes continuous actions into sequential discrete steps. It transforms the original MDP into an "inner MDP" between consecutive discrete steps and an "outer MDP" between states. SDQN uses two Q-networks - an inner Q-network to estimate state-action values for each discrete step, and an outer Q-network to estimate values between states. It updates the networks using Q-learning for the inner networks and regression to match the last inner Q to the outer Q. The method is tested on a multimodal environment and several MuJoCo tasks, outperform
Reinforcement Learning Guide For Beginnersgokulprasath06
Reinforcement Learning Guide:
Land in multiple job interviews by joining our Data Science certification course.
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Reinforcement learning (RL) is about finding an optimal policy that maximizes the expected cumulative reward. It works by having an agent interact with an uncertain environment and learn through trial-and-error using feedback in the form of rewards. There are two main learning methods in RL - Monte Carlo which learns from whole episodes and Temporal Difference learning which learns from successive states.
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.
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 summarizes key concepts in reinforcement learning:
- Agent-environment interaction is modeled as states, actions, and rewards
- A policy is a rule for selecting actions in each state
- The return is the total discounted future reward an agent aims to maximize
- Tasks can be episodic or continuing
- The Markov property means the future depends only on the present state
- The agent-environment framework can be modeled as a Markov decision process
The document discusses the key concepts behind Deep Q-Networks (DQN), a type of deep reinforcement learning algorithm. It begins with a brief overview of Q-learning and its limitations with large state/action spaces. It then covers the four main ideas of DQN: 1) Using a deep neural network to represent the Q-function instead of a table, 2) Optimizing the network weights using experience replay, 3) Using a separate target network to generate stable training targets, and 4) Storing experiences in a replay buffer to break correlations between consecutive states.
Deep reinforcement learning from scratchJie-Han Chen
1. The document provides an overview of deep reinforcement learning and the Deep Q-Network algorithm. It defines the key concepts of Markov Decision Processes including states, actions, rewards, and policies.
2. The Deep Q-Network uses a deep neural network as a function approximator to estimate the optimal action-value function. It employs experience replay and a separate target network to stabilize learning.
3. Experiments applying DQN to the Atari 2600 game Space Invaders are discussed, comparing different loss functions and optimizers. The standard DQN configuration with MSE loss and RMSProp performed best.
Reinforcement learning is a machine learning technique where an agent learns how to behave in an environment by receiving rewards or punishments for its actions. The goal of the agent is to learn an optimal policy that maximizes long-term rewards. Reinforcement learning can be applied to problems like game playing, robot control, scheduling, and economic modeling. The reinforcement learning process involves an agent interacting with an environment to learn through trial-and-error using state, action, reward, and policy. Common algorithms include Q-learning which uses a Q-table to learn the optimal action-selection policy.
This document is a final report for a CS799 course that explores using reinforcement learning to train an agent to play a chasing game. The author defines the game environment and mechanics, then uses Q-learning with an epsilon-greedy exploration strategy to train an agent to maximize its score by collecting vegetables while avoiding walls, minerals, and other players. The agent is trained in multiple phases to first avoid walls, then minerals, and finally other players while collecting vegetables. Results are presented comparing training with different exploration vs exploitation settings.
This document provides an overview of reinforcement learning concepts. It introduces reinforcement learning as using rewards to learn how to maximize utility. It describes Markov decision processes (MDPs) as the framework for modeling reinforcement learning problems, including states, actions, transitions, and rewards. It discusses solving MDPs by finding optimal policies using value iteration or policy iteration algorithms based on the Bellman equations. The goal is to learn optimal state values or action values through interaction rather than relying on a known model of the environment.
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
Matineh Shaker, Artificial Intelligence Scientist, Bonsai at MLconf SF 2017MLconf
This document discusses deep reinforcement learning and concept network reinforcement learning. It begins with an introduction to reinforcement learning concepts like Markov decision processes and value-based methods. It then describes Concept-Network Reinforcement Learning which decomposes complex tasks into high-level concepts or actions. This allows composing existing solutions to sub-problems without retraining. The document provides examples of using concept networks for lunar lander and robot pick-and-place tasks. It concludes by discussing how concept networks can improve sample efficiency, especially for sparse reward problems.
The document is a seminar report submitted by Kalaissiram S. for their Bachelor of Technology degree. It discusses reinforcement learning (RL), including the key concepts of agents, environments, actions, states, rewards, and policies. It also covers the Bellman equation, types of RL, Markov decision processes, popular RL algorithms like Q-learning and SARSA, and applications of RL.
This document provides an introduction to reinforcement learning. It defines reinforcement learning and compares it to machine learning. Key concepts in reinforcement learning are discussed such as policy, reward function, value function and environment. Examples of reinforcement learning applications include chess, robotics, petroleum refineries. Model-free and model-based methods are introduced. The document also discusses Monte Carlo methods, temporal difference learning, and Dyna-Q architecture. Finally, it provides examples of reinforcement learning problems like elevator dispatching and job shop scheduling.
This document provides an introduction to reinforcement learning. It defines reinforcement learning and compares it to supervised learning. Reinforcement learning involves an agent interacting with an environment and receiving rewards to learn a policy for maximizing rewards. The key elements of reinforcement learning problems are the agent, environment, state, actions, policy, reward function, and value function. The document discusses various reinforcement learning concepts like exploration vs exploitation, temporal difference learning, Q-learning, and Monte Carlo methods. It also compares model-based and model-free reinforcement learning approaches. Overall, the document provides a high-level overview of the main concepts and problem-solving methods in the field of reinforcement learning.
Andrii Prysiazhnyk: Why the amazon sellers are buiyng the RTX 3080: Dynamic p...Lviv Startup Club
Andrii Prysiazhnyk: Why the amazon sellers are buiyng the RTX 3080: Dynamic pricing with RL
AI & BigData Online Day 2021
Website - http://aiconf.com.ua
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
The Reinforcement Learning (RL) is a particular type of learning. It is useful when we try to learn from an unknown environment. Which means, that our model will have to explore the environment in order to collect the necessary data to use for its training. The model is represented as an Agent, trying to achieve a certain goal in a particular environment. The Agent affects this environment by taking actions that change the state of the environment and generate rewards produced by this later one.
The learning relies on the generated rewards, and the goal will be to maximize them. To choose the actions to apply, the agents use a policy. It can be defined as the process that the agent use to choose the actions that will permit it to optimize the overall rewards. In this course, we will see two methods used to develop these polices: policy gradient and Q-Learning. We will implement our examples using the following libraries: OpenAI gym, keras , tensorflow and keras-rl.
[Notebook 1](https://colab.research.google.com/drive/1395LU6jWULFogfErI8CIYpi35Y00YiRj)
[Notebook 2](https://colab.research.google.com/drive/1MpDS5rj-PwzzLIZtAGYnZ_jjEwhWZEdC)
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Discrete sequential prediction of continuous actions for deep RLJie-Han Chen
This paper proposes a method called SDQN (Sequential Deep Q-Network) to solve continuous action problems using a value-based reinforcement learning approach. SDQN discretizes continuous actions into sequential discrete steps. It transforms the original MDP into an "inner MDP" between consecutive discrete steps and an "outer MDP" between states. SDQN uses two Q-networks - an inner Q-network to estimate state-action values for each discrete step, and an outer Q-network to estimate values between states. It updates the networks using Q-learning for the inner networks and regression to match the last inner Q to the outer Q. The method is tested on a multimodal environment and several MuJoCo tasks, outperform
Reinforcement Learning Guide For Beginnersgokulprasath06
Reinforcement Learning Guide:
Land in multiple job interviews by joining our Data Science certification course.
Data Science course content designed uniquely, which helps you start learning Data Science from basics to advanced data science concepts.
Content: http://bit.ly/2Mub6xP
Any Queries, Call us@ +91 9884412301 / 9600112302
Reinforcement learning (RL) is about finding an optimal policy that maximizes the expected cumulative reward. It works by having an agent interact with an uncertain environment and learn through trial-and-error using feedback in the form of rewards. There are two main learning methods in RL - Monte Carlo which learns from whole episodes and Temporal Difference learning which learns from successive states.
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.
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 summarizes key concepts in reinforcement learning:
- Agent-environment interaction is modeled as states, actions, and rewards
- A policy is a rule for selecting actions in each state
- The return is the total discounted future reward an agent aims to maximize
- Tasks can be episodic or continuing
- The Markov property means the future depends only on the present state
- The agent-environment framework can be modeled as a Markov decision process
The document discusses the key concepts behind Deep Q-Networks (DQN), a type of deep reinforcement learning algorithm. It begins with a brief overview of Q-learning and its limitations with large state/action spaces. It then covers the four main ideas of DQN: 1) Using a deep neural network to represent the Q-function instead of a table, 2) Optimizing the network weights using experience replay, 3) Using a separate target network to generate stable training targets, and 4) Storing experiences in a replay buffer to break correlations between consecutive states.
This document discusses machine learning and how it can be implemented using Firebase MLKit. It begins with basic definitions of machine learning and its differences from traditional programming. It then introduces Firebase as a backend platform and discusses the advantages of performing machine learning on mobile devices for privacy and speed. The document outlines the typical steps to implement machine learning and how Firebase MLKit simplifies this process. It provides examples of machine learning models and capabilities available through Firebase MLKit like text recognition, face detection and image labeling. In the end, it encourages building apps using Firebase MLKit's features.
The document discusses Firebase Cloud Functions and how it provides a serverless backend platform. Some key points made include:
- Firebase Cloud Functions allow running code in response to events without having to manage servers. Common uses include sending emails, image processing, and handling secret tokens.
- With Firebase, events trigger Node.js functions that have full access to Google services like authentication, database, storage, and notifications through SDKs.
- Over 1.5 million apps currently use at least one Firebase feature, showing how Firebase enables building full backends without traditional server management.
This document discusses Android Pie and new features in digital wellbeing. It notes that Android has been in use for 10 years with over 2 billion monthly active users across 500 million devices. New features in Android Pie include adaptive battery to reduce CPU wake ups by 30%, adaptive brightness that learns user preferences, and app actions for predicting the next app launch or action. Digital wellbeing tools help users understand their device usage habits, focus on what matters most, and switch off to wind down from their devices.
This document discusses containers and Kubernetes. It explains that containers isolate applications and their dependencies to run anywhere. Containers share the host system's kernel, unlike virtual machines. Kubernetes is an open-source tool that provides automated management of containers at scale through desired state configuration and coordination of containerized applications. The document also mentions playing an orchestra with Kubernetes and getting containers to work on servers.
Android P is focused on intelligence, simplicity, and digital well-being. New features include on-device machine learning to optimize battery and brightness, integrating Google Lens and smart reply, and better screenshot capabilities. Digital well-being tools help users understand their app usage habits and focus on what matters by helping switch off distractions when needed.
This document discusses Android architecture components and patterns for developing maintainable Android applications. It introduces Model-View-Controller (MVC), then describes how Model-View-Presenter (MVP) and Model-View-ViewModel (MVVM) improve upon MVC. It outlines the benefits of separating concerns and driving UI from data models. Finally, it explains how the Android Architecture Components library implements MVVM patterns and components like ViewModel, LiveData, and Room for managing lifecycles, data binding and data access in Android apps.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
2. Hi
I am a Research Engineer in Applied RL
at InstaDeep and ML GDE
For the past the 3 years, I worked on
applying scalable DeepRL methods
application for placement on system
on chips and routing for printed circuit
boards
8. Why these models are labeled as smart
Machine Learning models are able to learn to make decisions to
achieve a predetermined goal
8
9. 9
Key types of Machine Learning tasks
Supervised
Learning
Unsupervised
Learning
Clustering
Association
Regression
Classification Translation
Identify population groups
Recommending products Recommending friends
Weather forecasting
Object
detection
Generate Image based
on latent Variables
10. 10
All tasks optimize a prediction loss
- Mean squared error loss
- Cross entropy loss
- Categorical cross entropy loss
- Cosine similarity loss
And many more ...
Using Stochastic gradient descent Algorithm
to optimize an objective function:
11. Tasks optimize for a goal by taking a
sequence of decision within an
environment
11
13. 13
Sequential decision making via Reinforcement Learning
Winning a chess game
- Optimise behavior based on a feedback signal (Reward)
- Learn an optimal behavior (policy) by interactions
with the world (environment) without provided examples
by interacting
- The feedback signal (Reward) on your actions can be
immediate or deferred (win or lose the game)
- The quality of the action you take depends on the current
state and the final outcome of the task (episode)
15. 15
1. The Reinforcement Learning framework
Environment
Agent
Reward
Next
observation
Action
- The agent interacts with an environment
within a finite horizon (episode)
- At each step t:
- Environment emits observation Oₜ
- Agent chooses an action Aₜ
- Environment executes the agent action
- Environment emits the reward Rₜ₊₁ and next observation Oₜ₊₁
16. The reward hypothesis
Any goal can be formalized as the outcome of maximizing a
cumulative reward
16
17. 17
2. The Rewards hypothesis
- A reward Rₜ indicates how well the agent is doing at timestep t
- The goal is to maximize the cumulative reward for the given task collected within an episode
- The episode return of state Sₜ depends on the sequence of actions that follows.
- The return can be discounted by 0 ≤ 𝛄 ≤ 1 to determine how much the agents cares about rewards in
the distant future relative to those in the immediate future.
For the rest of the presentation 𝛄 = 1
18. Estimators in maths
Estimation means having a rough calculation of the value, number,
quantity, or extent of something
18
19. 19
3. State Value function V(s)
- V(Sₜ) represents the expected return (cumulative reward) starting from state Sₜ and picking
actions following a policy
- Since we can define the return recursively
20. 20
3. State-Action Value function
4. State-Action Value function q(s,a)
- q(sₜ, a) represents the expected return (cumulative reward) starting from state sₜ and taking
action a then continue picking actions following a policy
- Given state action value function, we can derive a policy by picking the action corresponding to
highest Q value (Q-learning https://arxiv.org/abs/1312.5602)
20
21. 21
3. State-Action Value function
5. Agent observation
- The agent observation is a mapped from the environment state, Oₜ = f(Sₜ).
- The agent observation is not necessarily equal to the environment state.
- The environment is fully observable if Oₜ = Sₜ
- The environment is partially observable if Oₜʹ = Oₜ and Sₜʹ = Sₜ
21
Partially observed
environemnt
22. 22
- A mathematical formulation of the agent interaction with the environment
- It requires that the environment is fully observable
6. Markov decision process
- An MDP is a tuple (S, A, p, γ) where:
- S is the set of all possible states
- A is the set of all possible actions
- p(r,s′ | s,a) is the transition function or joint probability of a reward r and next state s′,
given a state s and action a
- γ ∈ [0, 1] is a discount factor that trades off later rewards to earlier ones
23. Markov decision principal
The Future is independent from the past given the present
The current state summarizes the history of the agent
23
24. 24
- Given the full horizon
Hₜ
7. Markovian state
- A state is called markovian only if
- If the environment is partially observable then the state is not Markovian
- We can turn a state to a Markovian state by stacking horizon data
Markovian state
Non Markovian state
25. Recap
- MDP is the representation of the agent-environment interaction
25
- Every RL problem can be formulated to a reward goal
- Agent components are: State, Value function, Policy, the world model
31. 31
3. State-Action Value function
1. Prediction and Control
- Prediction: given a policy, we can predict (evaluate) the future return given the current state
(learn value function)
- Control: improve your actions choices (learn policy function)
- Prediction and control can be strongly related
31
32. 32
3. State-Action Value function
2. Learning and Planning
- At first, the environment can be unknown to the agent.
- The agent learn the model of the world by interaction and exploration
- Once the model is learnt (sometime given ie: chess), the agent start planning actions to reach
optimal policy
32
35. 35
3. State-Action Value function
Tabular MDPs explained
- The state and action space is small enough to be represented by arrays or tables
- Given the exact quantification of the possible states and actions, we can find exactly the optimal
solution for the prediction (value function) and control (policy) problems
- 27 states
- 4 actions
- A reward of -1 for each step
35
37. Definition
The term dynamic programming (DP) refers to a collection of
algorithms that can be used to compute optimal policies given a
perfect model of the environment as a Markov decision process (MDP).
- Richard S.Sutton and Andrew G. Barto -
37
38. 38
3. State-Action Value function
1. Policy Evaluation
- Given an arbitrary policy π ,we want to compute the corresponding state value function V𝜋
- We iteratively iterate over all the states and update the state value using the equation
below until we reach a state of convergence
38
40. 40
3. State-Action Value function
2. Policy Improvement
- The goal of computing the value function for a policy is to help find a better policy
- Given the new value function, we can define the new policy
40
44. Notes
Monte Carlo methods require only experience—sample sequences of
states, actions, and rewards from actual or simulated interaction
with an environment without the need for the full probability
distribution of state, reward over actions
- Richard S.Sutton and Andrew G. Barto -
44
45. 45
3. State-Action Value function
1. First visit Monte-Carlo for prediction
- Given an arbitrary policy π ,we can estimate V𝜋
- Once the Algorithm converges we can move to policy improvement
- An acceptable estimate of Gₜ would be the average of all the encountered discounted
returns after infinite visits to the state Gₜ
45
47. 47
3. State-Action Value function
2. First visit Monte-Carlo for control
- Given an arbitrary initial policy π ,we can estimate state action value V𝜋.
- Instead of averaging the return of the visited state Sₜ, we average the return of the
visited state action pair Sₜ, Aₜ .
- The new policy can be calculated by choosing the action corresponding to best Q value
47
49. Exploration vs Exploitation problem
49
All learning control methods face a dilemma: they seek to learn
action values conditional on subsequent optimal behavior, but they
need to behave non-optimally in order to explore all actions
- Richard S.Sutton and Andrew G. Barto -
50. 50
3. State-Action Value function
Off policy and On policy methods
- Learning control methods fall into two categories: off policy and on policy methods
- On policy methods update the current policy using the data generated by the former (which
what we have been doing so far)
- Off policy methods update the current policy using data generated by two policies
- Target policy: the current policy being learned about
- Behavior policy: the policy responsible of generating a exploratory behavior ( random
actions, data generated by old policies )
50
51. 51
3. State-Action Value function
3. Monte-Carlo Generalized Policy Iteration
- Sample episode 1, . . ., k, . . ., using π: {S₁, A₂, R₂, ..., Sₜ } ∼ π
- For each state St and action At in the episode
- Improve policy based on new action-value function
51
52. Problems with MC methods
- High variance given
52
- Waiting until the end of the of the episode
54. 54
3. State-Action Value function
TD-learning explained
- TD-learning is a combination of monte-carlo and dynamic programming ideas.
- It is the backbone of most of state of the art Deep Reinforcement Learning algorithm DQN, PPO ...
- Like DP, TD-learning update the estimate based on another estimate. We call this Bootstrapping
- Like MC, TD-L learns directly from experiences without the need for a model of the environment.
54
55. 55
3. State-Action Value function
1. TD Prediction
- MC methods uses the episode return Gₜ as the target for the value for Sₜ.
- Unlike MC methods, TD methods update the value at each step and use an estimate of
Gₜ, we call the TD-Target.
55
57. 57
3. State-Action Value function
1. Example of MC vs TD prediction
- we are driving home from work and we try
to estimate how long it will take us.
- At each step, we re-estimated our time
because of complications (e.g. car
doesn’t work, highway is busy, etc).
- How can we update our estimate of the
time it takes to get home for next time
we leave work?
57
58. 58
3. State-Action Value function
1. Example of MC vs TD prediction
- we are driving home from work and we try
to estimate how long it will take us.
- At each step, we re-estimated our time
because of complications (e.g. car
doesn’t work, highway is busy, etc).
- How can we update our estimate of the
time it takes to get home for next time
we leave work?
Monte Carlo TD-Learning
58
59. 59
3. State-Action Value function
2. Sarsa: On Policy TD Control
- Similarly to MC method, we learn a policy by learning the action value function Q(S,A)
- The Algorithm is called Sarsa as it relies on transition { state, action, reward }
- Theis Algorithm is the backbone to the famous Deep Q-learning paper
59
63. 63
3. State-Action Value function
Dynamic programming
Policy Evaluation
Policy Improvement
Value Iteration
Tabular solution methods
Model based Model free
Monte Carlo methods
TD-learning methods
1. The family of tabular solution methods
63
65. OpenAI: solving the rubix cube using a single handed robot
- The robots observes the world
through camera lenses,
censors..ect
- The state space is infinite and
it's not practical to store in
a table
- The state space consists of a
set of unstructured data and
not tabular data
65
66. Deep neural network are the best fit for unstructured data
Function approximator
Action values
State value
State
Rubik's cube image
Linear or non
Linear function Output of the
function
66
68. Function derivatives and Gradient
68
- The derivative of a function f measure the sensitivity to change
with respect to the argument x
- The gradient of a function with respect to x, measure by how much x
needs to change so we reach a minimum
70. 70
1. Value function approximation
- Given a function approximator with a set of
weights 𝔀, minimize 𝘑(w)
- Using stochastic Gradient Descent
algorithms we form a good estimator for the
loss
- The loss target can be the MC return of the
TD target.
72. 72
3. State-Action Value function
Dynamic programming
Policy Evaluation
Policy Improvement
Value Iteration
Tabular solution
methods
Model based Model free
Monte Carlo methods
TD-learning methods
What we've learnt doay
Value approximation
Approximation
method
Policy gradient
72