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Deep Reinforcement Learning Hands-On - Third Edition

Name: Deep Reinforcement Learning Hands-On - Third Edition
Author: Maxim Lapan
ISBN: 9781835882702

by Maxim Lapan

November 2024

Intermediate to advanced

716 pages

19h 34m

English

Packt Publishing

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Preface
Why I wrote this bookThe approachWho this book is forWhat this book coversTo get the most out of this bookChanges in the third edition
Part 1 Introduction to RL
What Is Reinforcement Learning?
Supervised learningUnsupervised learningReinforcement learningComplications in RLRL formalismsRewardThe agentThe environmentActionsObservationsThe theoretical foundations of RLMarkov decision processesThe Markov processMarkov reward processesAdding actions to MDPPolicySummary
OpenAI Gym API and Gymnasium
The anatomy of the agentHardware and software requirementsThe OpenAI Gym API and GymnasiumThe action spaceThe observation spaceThe environmentCreating an environmentThe CartPole sessionThe random CartPole agentExtra Gym API functionalityWrappersRendering the environmentMore wrappersSummary
Deep Learning with PyTorch
TensorsThe creation of tensorsScalar tensorsTensor operationsGPU tensorsGradientsTensors and gradientsNN building blocksCustom layersLoss functions and optimizersLoss functionsOptimizersMonitoring with TensorBoardTensorBoard 101Plotting metricsGAN on Atari imagesPyTorch IgniteIgnite conceptsGAN training on Atari using IgniteSummary
The Cross-Entropy Method
The taxonomy of RL methodsThe cross-entropy method in practiceThe cross-entropy method on CartPoleThe cross-entropy method on FrozenLakeThe theoretical background of the cross-entropy methodSummary
Part 2 Value-based methods
Tabular Learning and the Bellman Equation
Value, state, and optimalityThe Bellman equation of optimalityThe value of the actionThe value iteration methodValue iteration in practiceQ-iteration for FrozenLakeSummary
Deep Q-Networks
Real-life value iterationTabular Q-learningDeep Q-learningInteraction with the environmentSGD optimizationCorrelation between stepsThe Markov propertyThe final form of DQN trainingDQN on PongWrappersThe DQN modelTrainingRunning and performanceYour model in actionThings to trySummary
Higher-Level RL Libraries
Why RL libraries?The PTAN libraryAction selectorsThe agentDQNAgentPolicyAgentExperience sourceToy environmentThe ExperienceSource classThe ExperienceSourceFirstLast ClassExperience replay buffersThe TargetNet classIgnite helpersThe PTAN CartPole solverOther RL librariesSummary

DQN Extensions
Basic DQNCommon libraryImplementationHyperparameter tuningResults with common parametersTuned baseline DQNN-step DQNImplementationResultsHyperparameter tuningDouble DQNImplementationResultsHyperparameter tuningNoisy networksImplementationResultsHyperparameter tuningPrioritized replay bufferImplementationResultsHyperparameter tuningDueling DQNImplementationResultsHyperparameter tuningCategorical DQNImplementationResultsHyperparameter tuningCombining everythingResultsHyperparameter tuningSummary
Ways to Speed Up RL
Why speed mattersBaselineThe computation graph in PyTorchSeveral environmentsPlaying and training in separate processesTweaking wrappersBenchmark resultsSummary
Stocks Trading Using RL
Why trading?Problem statement and key decisionsDataThe trading environmentModelsTraining codeResultsThe feed-forward modelThe convolution modelThings to trySummary
Part 3 Policy-based methods
Policy Gradients
Values and policyWhy the policy?Policy representationPolicy gradientsThe REINFORCE methodThe CartPole exampleResultsPolicy-based versus value-based methodsREINFORCE issuesFull episodes are requiredHigh gradient varianceExploration problemsHigh correlation of samplesPolicy gradient methods on CartPoleImplementationResultsPolicy gradient methods on PongImplementationResultsSummary
Actor-Critic Method: A2C and A3C
Variance reductionCartPole varianceAdvantage actor-critic (A2C)A2C on PongResultsAsynchronous Advantage Actor-Critic (A3C)Correlation and sample efficiencyAdding an extra “A” to A2CA3C with data parallelismResultsA3C with gradient parallelismImplementationResultsSummary
The TextWorld Environment
Interactive fictionThe environmentInstallationGame generationObservation and action spacesExtra game informationThe deep NLP basicsRecurrent Neural Networks (RNNs)Word embeddingThe Encoder-Decoder architectureTransformersBaseline DQNObservation preprocessingEmbeddings and encodersThe DQN model and the agentTraining codeTraining resultsTweaking observationsTracking visited roomsRelative actionsObjective in observationTransformersChatGPTSetupInteractive modeChatGPT APISummary
Web Navigation
The evolution of web navigationBrowser automation and RLChallenges in browser automationThe MiniWoB benchmarkMiniWoB++InstallationActions and observationsSimple exampleThe simple clicking approachGrid actionsThe RL part of our implementationThe model and training codeTraining resultsSimple clicking limitationsAdding text descriptionImplementationResultsHuman demonstrationsRecording the demonstrationsTraining with demonstrationsResultsThings to trySummary
Part 4 Advanced RL
Continous Action Space
Why a continuous space?The action spaceEnvironmentsThe A2C methodImplementationResultsUsing models and recording videosDeep deterministic policy gradientsExplorationImplementationResults and videoDistributional policy gradientsArchitectureImplementationResultsThings to trySummary
Trust Region Methods
EnvironmentsThe A2C baselineImplementationResultsVideo recordingPPOImplementationResultsTRPOImplementationResultsACKTRImplementationResultsSACImplementationResultsOverall resultsSummary
Black-Box Optimizations in RL
Black-box methodsEvolution strategiesImplementing ES on CartPoleCartPole resultsES on HalfCheetahImplementing ES on HalfCheetahHalfCheetah resultsGenetic algorithmsGA on CartPoleGA tweaksDeep GANovelty searchGA on HalfCheetahImplementationResultsSummary
Advanced Exploration
Why exploration is importantWhat’s wrong with 𝜖-greedy?Alternative ways of explorationNoisy networksCount-based methodsPrediction-based methodsMountainCar experimentsDQN + 𝜖-greedyDQN + noisy networksDQN + state countsPPO methodPPO + Noisy NetworksPPO + state countsPPO + network distillationComparison of methodsAtari experimentsDQN + 𝜖-greedyDQN + noisy networksPPOSummary
Reinforcement Learning with Human Feedback
Reward functions in complex environmentsTheoretical backgroundMethod overviewRLHF and LLMsRLHF experimentsInitial training using A2CLabeling processReward model trainingCombining A2C with the reward modelFine-tuning with 100 labelsThe second round of the experimentThe third round of the experimentOverall resultsSummary
AlphaGo Zero and MuZero
Comparing model-based and model-free methodsModel-based methods for board gamesThe AlphaGo Zero methodOverviewMCTSSelf-playTraining and evaluationConnect 4 with AlphaGo ZeroThe game modelImplementing MCTSThe modelTrainingTesting and comparisonResultsMuZeroHigh-level modelTraining processConnect 4 with MuZeroHyperparameters and MCTS tree nodesModelsMCTS searchTraining data and gameplayMuZero resultsMuZero and AtariSummary
RL in Discrete Optimization
The Rubik’s cube and discrete optimizationOptimality and God’s numberApproaches to cube solvingActionsStatesThe training processThe NN architectureThe trainingThe model applicationResultsThe code outlineCube environmentsTrainingThe search processThe experiment resultsThe 2 × 2 cubeThe 3 × 3 cubeFurther improvements and experimentsSummary
Multi-Agent RL
What is multi-agent RL?Getting started with the environmentAn overview of MAgentInstalling MAgentSetting up a random environmentDeep Q-network for tigersUnderstanding the codeTraining and resultsCollaboration by the tigersTraining both tigers and deerThe battle environmentSummary
Bibliography
Index

Content preview from Deep Reinforcement Learning Hands-On - Third Edition

20 AlphaGo Zero and MuZero

Model-based methods allow us to decrease the amount of communication with the environment by building a model of the environment and using it during training. In this chapter, we take a look at model-based methods by exploring cases where we have a model of the environment, but this environment is being used by two competing parties. This situation is very common in board games, where the rules of the game are fixed and the full position is observable, but we have an opponent who has the primary goal of preventing us from winning the game.

A few years ago, DeepMind proposed a very elegant approach to solving such problems. No prior domain knowledge is required, but the agent improves its policy only via self-play. ...

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Deep Reinforcement Learning Hands-On - Third Edition

by Maxim Lapan

20

AlphaGo Zero and MuZero

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.