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
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
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.
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
This document provides an overview of reinforcement learning including:
1. It defines reinforcement learning as a type of machine learning that enables agents to learn through trial-and-error using feedback from their actions and experiences.
2. It discusses an example of AWS Deepracer, which is a tool for learning reinforcement learning by racing autonomous cars in a simulated environment.
3. It explains key concepts in reinforcement learning including Markov decision processes, states, actions, rewards, policies, and value functions which are used to attain optimal solutions.
Why should you care about Markov Chain Monte Carlo methods?
→ They are in the list of "Top 10 Algorithms of 20th Century"
→ They allow you to make inference with Bayesian Networks
→ They are used everywhere in Machine Learning and Statistics
Markov Chain Monte Carlo methods are a class of algorithms used to sample from complicated distributions. Typically, this is the case of posterior distributions in Bayesian Networks (Belief Networks).
These slides cover the following topics.
→ Motivation and Practical Examples (Bayesian Networks)
→ Basic Principles of MCMC
→ Gibbs Sampling
→ Metropolis–Hastings
→ Hamiltonian Monte Carlo
→ Reversible-Jump Markov Chain Monte Carlo
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.
The document discusses the Naive REINFORCE algorithm for reinforcement learning. It belongs to the policy gradient class of algorithms. The algorithm works by iteratively updating a neural network policy to maximize the expected reward. It initializes a random policy network, runs episodes to collect rewards and action probabilities, calculates the discounted reward, and backpropagates the error to adjust the policy network weights to increase expected reward over time. Key aspects include directly updating the policy weights via policy gradients without using a value function, which results in slower learning than methods using value functions.
The document discusses multi-armed bandits and their applications. It provides an overview of multi-armed bandits, describing the exploration-exploitation dilemma. It then discusses the optimal UCB algorithm and how it balances exploration and exploitation. Finally, it summarizes two applications of multi-armed bandits: using them for learning to rank in recommendation systems and addressing the cold-start problem in recommender systems.
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
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.
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
This document provides an overview of reinforcement learning including:
1. It defines reinforcement learning as a type of machine learning that enables agents to learn through trial-and-error using feedback from their actions and experiences.
2. It discusses an example of AWS Deepracer, which is a tool for learning reinforcement learning by racing autonomous cars in a simulated environment.
3. It explains key concepts in reinforcement learning including Markov decision processes, states, actions, rewards, policies, and value functions which are used to attain optimal solutions.
Why should you care about Markov Chain Monte Carlo methods?
→ They are in the list of "Top 10 Algorithms of 20th Century"
→ They allow you to make inference with Bayesian Networks
→ They are used everywhere in Machine Learning and Statistics
Markov Chain Monte Carlo methods are a class of algorithms used to sample from complicated distributions. Typically, this is the case of posterior distributions in Bayesian Networks (Belief Networks).
These slides cover the following topics.
→ Motivation and Practical Examples (Bayesian Networks)
→ Basic Principles of MCMC
→ Gibbs Sampling
→ Metropolis–Hastings
→ Hamiltonian Monte Carlo
→ Reversible-Jump Markov Chain Monte Carlo
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.
The document discusses the Naive REINFORCE algorithm for reinforcement learning. It belongs to the policy gradient class of algorithms. The algorithm works by iteratively updating a neural network policy to maximize the expected reward. It initializes a random policy network, runs episodes to collect rewards and action probabilities, calculates the discounted reward, and backpropagates the error to adjust the policy network weights to increase expected reward over time. Key aspects include directly updating the policy weights via policy gradients without using a value function, which results in slower learning than methods using value functions.
The document discusses multi-armed bandits and their applications. It provides an overview of multi-armed bandits, describing the exploration-exploitation dilemma. It then discusses the optimal UCB algorithm and how it balances exploration and exploitation. Finally, it summarizes two applications of multi-armed bandits: using them for learning to rank in recommendation systems and addressing the cold-start problem in recommender systems.
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 document discusses rule-based classification. It describes how rule-based classification models use if-then rules to classify data. It covers extracting rules from decision trees and directly from training data. Key points include using sequential covering algorithms to iteratively learn rules that each cover positive examples of a class, and measuring rule quality based on both coverage and accuracy to determine the best rules.
Reinforcement Learning 4. Dynamic ProgrammingSeung Jae Lee
A summary of Chapter 4: Dynamic Programming of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
( 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
This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
Reinforcement Learning 5. Monte Carlo MethodsSeung Jae Lee
A summary of Chapter 5: Monte Carlo Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
This document summarizes and compares two popular Python libraries for graph neural networks - Spektral and PyTorch Geometric. It begins by providing an overview of the basic functionality and architecture of each library. It then discusses how each library handles data loading and mini-batching of graph data. The document reviews several common message passing layer types implemented in both libraries. It provides an example comparison of using each library for a node classification task on the Cora dataset. Finally, it discusses a graph classification comparison in PyTorch Geometric using different message passing and pooling layers on the IMDB-binary dataset.
https://telecombcn-dl.github.io/2017-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.
The document discusses reinforcement learning and Markov decision processes. It introduces reinforcement learning as an approach to machine learning where an agent learns to take actions in an environment to maximize rewards. Markov decision processes are described as a framework involving states, actions, transitions between states, and rewards. The goals of reinforcement learning are to learn a policy that maps states to optimal actions to maximize long-term rewards.
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.
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
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.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
Reinforcement Learning 7. n-step BootstrappingSeung Jae Lee
A summary of Chapter 7: n-step Bootstrapping of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
Artificial Intelligence: What Is Reinforcement Learning?Bernard Marr
Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this SlideShare, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today.
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.
Recurrent neural networks (RNNs) are well-suited for analyzing text data because they can model sequential and structural relationships in text. RNNs use gating mechanisms like LSTMs and GRUs to address the problem of exploding or vanishing gradients when training on long sequences. Modern RNNs trained with techniques like gradient clipping, improved initialization, and optimized training algorithms like Adam can learn meaningful representations from text even with millions of training examples. RNNs may outperform conventional bag-of-words models on large datasets but require significant computational resources. The author describes an RNN library called Passage and provides an example of sentiment analysis on movie reviews to demonstrate RNNs for text analysis.
The document describes algorithms for solving geometric problems in computational geometry. It discusses algorithms for determining if line segments intersect in O(n log n) time using a sweep line approach. It also describes using the cross product to compare orientations of segments and determine if consecutive segments make a left or right turn.
DBScan stands for Density-Based Spatial Clustering of Applications with Noise.
DBScan Concepts
DBScan Parameters
DBScan Connectivity and Reachability
DBScan Algorithm , Flowchart and Example
Advantages and Disadvantages of DBScan
DBScan Complexity
Outliers related question and its solution.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
This document provides an overview of deep reinforcement learning and related concepts. It discusses reinforcement learning techniques such as model-based and model-free approaches. Deep reinforcement learning techniques like deep Q-networks, policy gradients, and actor-critic methods are explained. The document also introduces decision transformers, which transform reinforcement learning into a sequence modeling problem, and multi-game decision transformers which can learn to play multiple games simultaneously.
SEARN is an algorithm for structured prediction that casts it as a sequence of cost-sensitive classification problems. It works by learning a policy to make incremental decisions that build up the full structured output. The policy is trained through an iterative process of generating cost-sensitive examples from sample outputs produced by the current policy, training a classifier on those examples, and interpolating the new policy with the previous one. This allows SEARN to learn the structured prediction task without requiring assumptions about the output structure, unlike approaches that make independence assumptions or rely on global prediction models.
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 document discusses rule-based classification. It describes how rule-based classification models use if-then rules to classify data. It covers extracting rules from decision trees and directly from training data. Key points include using sequential covering algorithms to iteratively learn rules that each cover positive examples of a class, and measuring rule quality based on both coverage and accuracy to determine the best rules.
Reinforcement Learning 4. Dynamic ProgrammingSeung Jae Lee
A summary of Chapter 4: Dynamic Programming of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
( 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
This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
Reinforcement Learning 5. Monte Carlo MethodsSeung Jae Lee
A summary of Chapter 5: Monte Carlo Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
This document summarizes and compares two popular Python libraries for graph neural networks - Spektral and PyTorch Geometric. It begins by providing an overview of the basic functionality and architecture of each library. It then discusses how each library handles data loading and mini-batching of graph data. The document reviews several common message passing layer types implemented in both libraries. It provides an example comparison of using each library for a node classification task on the Cora dataset. Finally, it discusses a graph classification comparison in PyTorch Geometric using different message passing and pooling layers on the IMDB-binary dataset.
https://telecombcn-dl.github.io/2017-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.
The document discusses reinforcement learning and Markov decision processes. It introduces reinforcement learning as an approach to machine learning where an agent learns to take actions in an environment to maximize rewards. Markov decision processes are described as a framework involving states, actions, transitions between states, and rewards. The goals of reinforcement learning are to learn a policy that maps states to optimal actions to maximize long-term rewards.
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.
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
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.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
Reinforcement Learning 7. n-step BootstrappingSeung Jae Lee
A summary of Chapter 7: n-step Bootstrapping of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
Artificial Intelligence: What Is Reinforcement Learning?Bernard Marr
Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this SlideShare, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today.
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.
Recurrent neural networks (RNNs) are well-suited for analyzing text data because they can model sequential and structural relationships in text. RNNs use gating mechanisms like LSTMs and GRUs to address the problem of exploding or vanishing gradients when training on long sequences. Modern RNNs trained with techniques like gradient clipping, improved initialization, and optimized training algorithms like Adam can learn meaningful representations from text even with millions of training examples. RNNs may outperform conventional bag-of-words models on large datasets but require significant computational resources. The author describes an RNN library called Passage and provides an example of sentiment analysis on movie reviews to demonstrate RNNs for text analysis.
The document describes algorithms for solving geometric problems in computational geometry. It discusses algorithms for determining if line segments intersect in O(n log n) time using a sweep line approach. It also describes using the cross product to compare orientations of segments and determine if consecutive segments make a left or right turn.
DBScan stands for Density-Based Spatial Clustering of Applications with Noise.
DBScan Concepts
DBScan Parameters
DBScan Connectivity and Reachability
DBScan Algorithm , Flowchart and Example
Advantages and Disadvantages of DBScan
DBScan Complexity
Outliers related question and its solution.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
This document provides an overview of deep reinforcement learning and related concepts. It discusses reinforcement learning techniques such as model-based and model-free approaches. Deep reinforcement learning techniques like deep Q-networks, policy gradients, and actor-critic methods are explained. The document also introduces decision transformers, which transform reinforcement learning into a sequence modeling problem, and multi-game decision transformers which can learn to play multiple games simultaneously.
SEARN is an algorithm for structured prediction that casts it as a sequence of cost-sensitive classification problems. It works by learning a policy to make incremental decisions that build up the full structured output. The policy is trained through an iterative process of generating cost-sensitive examples from sample outputs produced by the current policy, training a classifier on those examples, and interpolating the new policy with the previous one. This allows SEARN to learn the structured prediction task without requiring assumptions about the output structure, unlike approaches that make independence assumptions or rely on global prediction models.
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.
Dexterous In-hand Manipulation by OpenAIAnand Joshi
OpenAI has used Reinforcement Learning to train a humanoid robotic hand to rotate a cube to achieve any desired orientation. This is discussed in arXiv:1808.00177, 2019 and in the blog <openai.com/blog/learning dexterity/>. These slides present results from the paper along with a few important concepts in reinforcement learning I learnt through many other sources.
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 8: Planning and Learning with Tabular MethodsSeung Jae Lee
A summary of Chapter 8: Planning and Learning with Tabular Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
This document provides an introduction to reinforcement learning. It defines reinforcement learning as finding a policy that maximizes the sum of rewards by interacting with an environment. It discusses key concepts like Markov decision processes, value functions, temporal difference learning, Q-learning, and deep reinforcement learning. The document also provides examples of applications in games, robotics, economics and comparisons of model-based planning versus model-free reinforcement learning approaches.
Temporal-difference (TD) learning combines ideas from Monte Carlo and dynamic programming methods. It updates estimates based in part on other estimates, like dynamic programming, but uses sampling experiences to estimate expected returns, like Monte Carlo. TD learning is model-free, incremental, and can be applied to continuing tasks. The TD error is the difference between the target value and estimated value, which is used to update value estimates through methods like Sarsa and Q-learning. N-step TD and TD(λ) generalize the idea by incorporating returns and eligibility traces over multiple steps.
Lecture slides of DSAI 2018 in National Cheng Kung University.
Reinforcement Learning: Temporal-difference Learning, including Sarsa, Q-learning, n-step bootstrapping, eligibility trace.
1. The document describes the Behavior Regularized Actor Critic (BRAC) framework, which evaluates different design choices for offline reinforcement learning algorithms.
2. BRAC experiments show that simple variants using a fixed regularization weight, minimum ensemble Q-targets, and value penalty regularization can achieve good performance, outperforming more complex techniques from previous work.
3. The experiments find that choices like the divergence used for regularization and number of ensemble Q-functions do not have large impacts on performance, and hyperparameter sensitivity also varies between design choices.
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.
1. Policy gradient methods estimate the optimal policy through gradient ascent on the expected return. They directly learn stochastic policies without estimating value functions.
2. REINFORCE uses Monte Carlo returns to estimate the policy gradient. It updates the policy parameters in the direction of the gradient to maximize expected returns.
3. PPO improves upon REINFORCE by clipping the objective function to restrict how far the new policy can be from the old policy, which helps stabilize training. It uses a surrogate objective and importance sampling to train the policy on data collected from previous policies.
Reinforcement Learning Guide For Beginnersgokulprasath06
Reinforcement Learning Guide:
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A presentation about NGBoost (Natural Gradient Boosting) which I presented in the Information Theory and Probabilistic Programming course at the University of Oklahoma.
Presentazione Tesi Laurea Triennale in InformaticaLuca Marignati
Università degli Studi di Torino
Dipartimento di Informatica
Titolo: Apprendimento per Rinforzo e Applicazione ai Problemi di Pianificazione del Percorso
Topic: Machine Learning
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
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
Modern Recommendation for Advanced Practitioners part2Flavian Vasile
This document summarizes a section on policy learning approaches for recommendation systems. It begins by contrasting policy-based models with value-based models, noting that policy models directly learn a mapping from user states to actions rather than computing value estimates for all actions.
It then introduces concepts in contextual bandits and reinforcement learning, noting that contextual bandits are often a better fit for recommendations since recommendations typically have independent effects. It also discusses using counterfactual risk minimization to address covariate shift in policy learning models by reweighting training data based on logging and target policies.
Finally, it proposes two formulations for contextual bandit models for recommendations - one that directly optimizes a clipped importance sampling objective, and one that optimizes
Policy Based reinforcement Learning for time series Anomaly detectionKishor Datta Gupta
This document discusses a policy-based reinforcement learning approach called PTAD for time series anomaly detection. PTAD formulates anomaly detection as a Markov Decision Process and uses an asynchronous actor-critic algorithm to learn a stochastic policy. The agent takes as input current and previous time series data and actions, and outputs a decision of normal or anomalous. It is rewarded based on a confusion matrix calculation. Experimental results show PTAD achieves best performance both within and across datasets by adjusting to different behaviors. The stochastic policy allows exploring precision-recall tradeoffs. While interesting, it is not compared to neural network based techniques like autoencoders.
Similar to Reinforcement learning:policy gradient (part 1) (20)
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
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Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
3. Reinforcement Learning Classification
● Value-Based
○ Learned Value Function
○ Implicit Policy
(usually Ɛ-greedy)
● Policy-Based
○ No Value Function
○ Explicit Policy
Parameterization
● Mixed(Actor-Critic)
○ Learned Value Function
○ Policy Parameterization
3
5. Policy Approximation (Discrete Actions)
● Ensure exploration we generally require that the policy never becomes
deterministic
● The most common parameterization for discrete action spaces - Softmax
in action preferences
○ discrete action space can not too large
● Action preferences can be
parameterization arbitrarily(linear, ANN...)
5
6. Advantage of Policy Approximation
1. Can approach to a deterministic policy (Ɛ-
greedy always has Ɛ probability of selecting
a random action),ex: Temperature parameter
(T -> 0) of soft-max
○ In practice, it is difficult to choose reduction
schedule or initial value of T
2. Enables the selection of actions with
arbitrary probabilities
○ Bluffing in poker, Action-Value methods have no
natural way
6
7. https://en.wikipedia.org/wiki/Softmax_function (Temperature parameters)
3. May be simpler function to approximate depending on the complexity
of
policies and action-value functions
4. A good way of injecting prior knowledge about the desired form of the
policy into the reinforcement learning system (often the most important
reason)
7
8. Short Corridor With Switched Actions
● All the states appear
identical under the function
approximation
● A method can do
significantly better if it can
learn a specific probability
with which to select right
● The best probability is
about 0.59
8
9. The Policy Gradient Theorem (Episodic)
https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-
learning-with-function-approximation.pdf
NIPS 2000 Policy Gradient Methods for Reinforcement Learning with Function
Approximation (Richard S. Sutton)
9
10. The Policy Gradient Theorem
● Stronger convergence of guarantees are available for policy-gradient
method than for action-value methods
○ Ɛ-greedy selection may change dramatically for an arbitrary small
action value change that results in having the maximal value
● There are two cases define the different performance messures
○ Episodic Case - performance measure as the value of the start state
of the episode
○ Continuing Case - no end even start state (Refer to Chap10.3)
10
15. The Policy Gradient Theorem (Episodic) - On Policy Distribution
fraction of time spent in s that is
usually under on-policy training
(on-policy distribution, the same
as p.43)
15
better be writed in
16. The Policy Gradient Theorem (Episodic) - On Policy Distribution
Number of time steps spent, on average, in
state s in a single episoid
h(s) denotes the probability that
an episode begins in states in a
single episode
16
17. The Policy Gradient Theorem (Episodic) - Concept
17
Ratio of s that
appear in the
state-action tree
Gathering gradients over all action
spaces of every state
18. The Policy Gradient Theorem (Episodic):
Sum Over States Weighted by How Offen the States Occur Under The
Policy
● Policy gradient for episodic case
● The distribution is the on-policy distribution under
● The constant of proportionality is the average length of an episode and
can be absorbed to step size
● Performance’s gradient ascent does not involve the derivative of the state
distribution
18
21. REINFORCE Meaning
● The update increases the
parameter vector in this
direction proportional
to the return
● inversely proportional to the
action probability (make sense
because otherwise actions that
are selected frequently are at an
advantage)
Action is a summation. If using
samping by action probability, we
have to average gradient by sampling
number
21
23. REINFORCE on the short-corridor gridworld
short-corridor gridworld
● With a good step
size, the total
reward per episode
approaches the
optimal value of the
start state
23
24. REINFORCE Defect & Solution
● Slow Converage
● High Variance From Reward
● Hard To Choose Learning Rate
24
25. REINFORCE with Baseline (episodic)
● Expected value of the update
unchanged(unbiased), but it
can have a large effect on its
variance
● Baseline can be any function,
evan a random variable
● For MDPs, the baseline should
vary with state, one natural
choice is state value function
○ some states all actions
have high values => a high
baseline
○ in others, => a low baseline
Treat State-Value Function as
a Independent Value-function
Approximation!
25
26. REINFORCE with Baseline (episodic)
can be learned by any methods
of previous chapters independently.
We use the same Monte-Carlo
here.(Section 9.3 Gradient Monte-Carlo)
26
27. Short-Corridor GridWorld
● Learn much
faster
● Policy
parameter is
much less clear
to set
● State-Value
function
paramenter
(Section 9.6)
27
28. Defects
● Learn Slowly (product estimates of high variance)
● Incovenient to implement online or continuing problems
28
30. One-Step Actor-Critic Method
● Add One-step
bootstrapping to make
it online
● But TD Method always
introduces bias
● The TD(0) with only
one random step has
lower variance than
Monte-Carlo and
accelerate learning 30
31. Actor-Critic
● Actor - Policy
Function
● Critic- State-Value
Function
● Critic Assign Credit
to Cricitize Actor’s
Selection
31
https://cs.wmich.edu/~trenary/files/cs5300/RLBook/node66.html
33. Actor-Critic with Eligiblity Traces (episodic)
● Weight Vector
is a long-term
memory
● Eligibility trace
is a short-term
memory,
keeping track
of which
components of
the weight
vector have
contributed to
recent state
33
38. The Policy Gradient Theorem (Continuing) - Performance Measure with Ergodicity
● “Ergodicity Assumption”
○ Any early decision by
the agent can have
only a temporary
effect
○ State Expectation in
the long run depends
on policy and MDP
transition
probabilities
○ Steady state
distribution is
assumed to exist and
to be independent of S0
guarantee
limit exist
Average Rate of Reward per Time Step
38
( is a fixed parameter for any . We will
treat it later as a linear function independent of
s in the theorem)
V(s)
39. The Policy Gradient Theorem (Continuing) - Performance Measure Definition
“Every Step’s Average
Reward Is The Same”39
40. The Policy Gradient Theorem (Continuing) - Steady State Distribution
Steady State Distribution Under
40
41. Replace Discount with Average Reward for Continuing Problem(Session 10.3, 10.4)
● Continuing problem with discounted setting is useful in tabular case, but
questionable for function approximation case
● In Continuing problem, performance measure with discounted setting is
proportional to the average reward setting (They has almost the same
effect )(session 10.4)
● Discounted setting is problematic with function approximation
○ with function approximation we have lost the policy improvement
theorem (session 4.3) important in Policy Iteration Method
41
42. Proof The Policy Gradient Theorem (Continuing) 1/2
Gradient Definition
Parameterization of policy
by replacing discount with
average reward setting 42
43. Proof The Policy Gradient Theorem (Continuing) 2/2
● Introduce
steady state
distribution
and its
property
steady state distribution property
43
By Definistion, it’s independent of s
Trick
46. Actor-Critic with Eligibility Traces (continuing)
● Replace Discount
with average
reward
● Traing with
Semi-Gradient
TD(0)
Independent Semi-Gradient TD(0)
=1
46
47. Policy Parameterization for Continuous
Actions
● Can deal with large or infinite
continue actions spaces
● Normal distribution of the actions are
through the state’s parameterization
Feature vectors constructed by Polynomial, Fourier... (Session 9.5) 47
Make it Positive
48. Chapter 19 Summary
● Policy gradient is superior to Ɛ-greedy and action-value method in
○ Can learn specific probabilities for taking the actions
○ Can approach deterministic policies asymptotically
○ Can naturally handle continuous action spaces
● Policy gradient theorem gives an exact formula for how performance is a
affected by the policy parameter that does not involve derivatives of the state
distribution.
● REINFORCE method
○ Add State-Value as Baseline -> reduce variance without introducing bias
● Actor-Critic method
○ Add state-value function for bootstrapping ->introduce bias but reduce
variance and accelerate learning
○ Critic assign credit to cricitize Actor’s selection
48
50. Comparison with Stochastic Policy Gradient
Advantage
● No action space sampling, more efficient (usually 10x faster)
● Can deal with large action space more efficiently
Weekness
● Less Exploration
50
51. Deterministic Policy Gradient Theorem - Performance Measure
● Deterministic Policy
Performance Messure
51
● Policy Gradient (Continuing)
Performance Messure
(Paper Not Distinguishing from
Episodic to Continuing Case)
Similar to
V(s)
V(s)
53. Deterministic Policy Gradient Theorem
Policy Gradient Theorem
Transition Probability
is parameterized by
Policy Gradient Theorem
53
Reward is
parameterized
by
58. Deterministic Policy Gradient Theorem vs Policy Gradient Theorem (episodic)
58
Both samping from
steady distribution,
but PG has to sum
over all acton spaces
Samping Space Samping Space
59. On-Policy Deterministic Actor-Critic Problems
59
● Behaving according to a deterministic policy will not ensure adequate
exploration and may lead to suboptimal solutions
● It may be useful for environments in which there is sufficient noise in the
environment to ensure adequate exploration, even with a deterministic
behaviour policy
● On-policy is not practical; may be useful for environments in which
there is sufficient noise in the environment to ensure adequate
exploration
Sarsa Update
60. Off-Policy Deterministic Actor-Critic (OPDAC)
● Original Deterministic target policy
µθ(s)
● Trajectories generated by an
arbitrary stochastic behaviour policy
β(s,a)
● Value-action function off-policy
update - Q learning
60
Off Policy Actor-Critic (using Importance
Sampling in both Actor and Critic)
https://arxiv.org/pdf/1205.4839.pdf
Off Policy Deterministic Actor-Critic
DAC removes the integral
over actions, so we can avoid
importance sampling in the
actor
63. Experiments Designs
63
1. Continus Bandit, with fixed width Gaussian
behaviro policy
2. Mountain Car, with fixed width
Gaussian behavior policy
3. Octopus Arm with 6 segments
a. Sigmoidal multi-layer perceptron (8 hidden units and sigmoidal output units) to represent
the policy (s)
b. A(s) function approximator (session 4.3)
c. V(s) multi-layer perceptron (40 hidden units and linear output units).
64. Experiment Results
64
In practice, the DAC significantly outperformed its stochastic counterpart by several
orders of magnitude in a bandit with 50 continuous action dimensions, and solved a
challenging reinforcement learning problem with 20 continuous action dimensions
and 50 state dimensions.
65. Deep Deterministic Policy
Gradient (DDPG)
https://arxiv.org/pdf/1509.02971.pdf
ICLR 2016 Continuous Control With Deep Reinforcement Learning (DeepMind)
65
66. Q-Learning Limitation
66
http://doremi2016.logdown.com/posts/2017/01/25/convolutional-neural-networks-cnn my cnn architecture
http://www.davidqiu.com:8888/research/nature14236.pdf Human-level control through deep reinforcement learning
Tabular Q-learning Limitations
● Very limited states/actions
● Can’t generalize to unobserved states
Q-learning with function approximation(neural net) can solve limits
above but still unstable or diverge
● The correlations present in the sequence of observations
● Small updates to Q function may significantly change the
policy(policy may oscillate)
● Scale of rewards vary greatly from game to game
○ lead to largely unstable gradient caculation
67. Deep Q-Learning
67http://www.davidqiu.com:8888/research/nature14236.pdf Human-level control through deep reinforcement learning
1. Experimence Replay
○ Break samples’
correlations
○ Off-policy learn for
all past policies
2. Independent Target Q-
network and update
weight from Q-network
every C steps
○ Avoid oscillations
○ Break correlations
with Q-network
3. Clip rewards to limit the
scale of TD error
○ Robust Gradinet
behavior policy Ɛ-greedy
experience replay buffer
Freeze and update Target Q network
train
minibach
size samples
69. DQN Flow (cont.)
69
1. Each time step, using Ɛ-greedy from Q-Network to creating samples and
assign to the experience buffer
2. Each Time Step, Experience Buffer randomly assign mini batch samples to
all networks(Q Network, Target Network Q’)
3. Calculate Q Network’s TD error. Update Q Network and target network
Q’(every C steps)
70. DQN Disadvantage
● Many tasks of interest, most notably physical control tasks, have
continuous (real valued) and high dimensional action spaces
● With high-dimensional observation spaces, it can only handle discrete and
low-dimensional action spaces (requires an iterative optimization process
at every step to find the argmax)
● Simple Approach for DQN to deal with continus domain is simply
discretizing,but many limitation:the number of actions increases
exponentially with the number of degrees of freedom,ex:a 7 degree of
freedom system (as in the human arm) with the coarsest discretization a ∈
{−k, 0, k} for each joint. 3^7 = 2187 action dimensionality
70
https://arxiv.org/pdf/1509.02971.pdf CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING
71. DDPG Contributions (DQN+DPG)
71
● Can learn policies “end-to-end”:directly from raw pixel inputs (DQN)
● Can learn policies from high-dimentional continus action space (DPG)
● From above, we can learn policies in large state and action space online
72. 72
DDPG Algo
● Experience
Replay
● Independent
Target
networks
● Batch
Normalization
of Minibatch
● Temporal
Correlated
Exploration
temporal correlated random policy
experience replay buffer
mini batch
Train Actor
mini batch
Train Critic
weighted blending between Q and Target Q’ network
weighted blending between Actor μ and Target Actor μ’network
74. DDPG Flow (cont.)
74
1. Each time step, using temporally correlated policy to create a sample and
assign it to experience replay buffer
2. Each time step, experience buffer assign mini batch samples to all
networks(Actor μ, Actor Target μ’, Q Network, Q' Target Network)
3. Calculate Q Network’s TD error. Update Q Network and Q' target network
Calculate Actor’s gradient. Update μ and target μ’
75. DDPG Challenges and Solutions
75
● Replay Buffer is used to break up sequeitial samples (like DQN)
● Target Networks is used for stable learning, but use “soft” update
○
○ Target networks slowly change, but greatly improve the stability of
learning
● Using Batch Normalization to normalize each dimension across the
minibatch samples (in low dimensional feature space, observation may
have different physical values, like position and velocity)
● Use Ornstein-Uhlenbeck process to generate temporally correlated
exploration efficiency with inertia
Learn a parameterized policy that can select actions without consulting a value function
value function may still be used to learn the policy parameter, but is not required for action selection
不失一般性,S0在每個episodic是固定的(non-random)
J(theta)在episodic和continuing不一樣
In problems with significant function approximation, the best approximate policy may be stochastic
In the latter case, a policy-based method will typically learn faster and yield a superior asymptotic policy(Kothiyal 2016)
policy change parameters will affect pi , reward, p. pi and reward is easy to calculate, but p is belong to environment(unknown)
policy gradient theorem will try to not involve the derivative of state distribution(p)
值函數近似(value-function approximation)的原理都是由Mean Squared Value Error中來的(session 9.2)
expectations are conditioned on the initial state, S0,侷限在以S0為起點所遍歷過(Ergodicity)的所有狀態下的期望值,沒有辦法經歷到的(也許是其他S為起點才能經歷到的狀態),不在M(s)範圍內
J(theta)=r(pi)的定義,是用來假設有個定值,而且會獨立於s(推導會用到),在theorem推導時,不會用這個definition,只會把它當一個線性變數,
N is nose - used an Ornstein-Uhlenbeck process (Uhlenbeck & Ornstein, 1930) to generate temporally correlated exploration for exploration efficiency in physical control problems with inertia