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The Reinforcement Learning Workshop
Variousintelligentapplicationssuchasvideogames,inventorymanagementsoftware,warehouserobots,andtranslationtoolsusereinforcementlearning(RL)tomakedecisionsandperformactionsthatmaximizetheprobabilityofthedesiredoutcome.ThisbookwillhelpyoutogettogripswiththetechniquesandthealgorithmsforimplementingRLinyourmachinelearningmodels.StartingwithanintroductiontoRL,you’llbeguidedthroughdifferentRLenvironmentsandframeworks.You’lllearnhowtoimplementyourowncustomenvironmentsanduseOpenAIbaselinestorunRLalgorithms.Onceyou’veexploredclassicRLtechniquessuchasDynamicProgramming,MonteCarlo,andTDLearning,you’llunderstandwhentoapplythedifferentdeeplearningmethodsinRLandadvancetodeepQ-learning.Thebookwillevenhelpyouunderstandthedifferentstagesofmachine-basedproblem-solvingbyusingDARQNonapopularvideogameBreakout.Finally,you’llfindoutwhentouseapolicy-basedmethodtotackleanRLproblem.BytheendofTheReinforcementLearningWorkshop,you’llbeequippedwiththeknowledgeandskillsneededtosolvechallengingproblemsusingreinforcementlearning.
目錄(102章)
倒序
- 封面
- 版權信息
- Preface
- 1. Introduction to Reinforcement Learning
- Introduction
- Learning Paradigms
- Fundamentals of Reinforcement Learning
- Reinforcement Learning Frameworks
- Applications of Reinforcement Learning
- Summary
- 2. Markov Decision Processes and Bellman Equations
- Introduction
- Markov Processes
- 3. Deep Learning in Practice with TensorFlow 2
- Introduction
- An Introduction to TensorFlow and Keras
- How to Implement a Neural Network Using TensorFlow
- Simple Regression Using TensorFlow
- Simple Classification Using TensorFlow
- TensorBoard – How to Visualize Data Using TensorBoard
- Summary
- 4. Getting Started with OpenAI and TensorFlow for Reinforcement Learning
- Introduction
- OpenAI Gym
- OpenAI Universe – Complex Environment
- TensorFlow for Reinforcement Learning
- OpenAI Baselines
- Training an RL Agent to Solve a Classic Control Problem
- Summary
- 5. Dynamic Programming
- Introduction
- Solving Dynamic Programming Problems
- Identifying Dynamic Programming Problems
- Dynamic Programming in RL
- Summary
- 6. Monte Carlo Methods
- Introduction
- The Workings of Monte Carlo Methods
- Understanding Monte Carlo with Blackjack
- Types of Monte Carlo Methods
- Exploration versus Exploitation Trade-Off
- Importance Sampling
- Solving Frozen Lake Using Monte Carlo
- Summary
- 7. Temporal Difference Learning
- Introduction to TD Learning
- TD(0) – SARSA and Q-Learning
- N-Step TD and TD(λ) Algorithms
- The Relationship between DP Monte-Carlo and TD Learning
- Summary
- 8. The Multi-Armed Bandit Problem
- Introduction
- Formulation of the MAB Problem
- The Python Interface
- The Greedy Algorithm
- The Explore-then-Commit Algorithm
- The ε-Greedy Algorithm
- The UCB algorithm
- Thompson Sampling
- Contextual Bandits
- Summary
- 9. What Is Deep Q-Learning?
- Introduction
- Basics of Deep Learning
- Basics of PyTorch
- The Action-Value Function (Q Value Function)
- Deep Q Learning
- Challenges in DQN
- Summary
- 10. Playing an Atari Game with Deep Recurrent Q-Networks
- Introduction
- Understanding the Breakout Environment
- CNNs in TensorFlow
- Combining a DQN with a CNN
- RNNs in TensorFlow
- Building a DRQN
- Introduction to the Attention Mechanism and DARQN
- Summary
- 11. Policy-Based Methods for Reinforcement Learning
- Introduction
- Policy Gradients
- Deep Deterministic Policy Gradients
- Improving Policy Gradients
- Summary
- 12. Evolutionary Strategies for RL
- Introduction
- Problems with Gradient-Based Methods
- Introduction to Genetic Algorithms
- Summary
- Appendix
- 1. Introduction to Reinforcement Learning
- 2. Markov Decision Processes and Bellman Equations
- 3. Deep Learning in Practice with TensorFlow 2
- 4. Getting started with OpenAI and TensorFlow for Reinforcement Learning
- 5. Dynamic Programming
- 6. Monte Carlo Methods
- 7. Temporal Difference Learning
- 8. The Multi-Armed Bandit Problem
- 9. What Is Deep Q-Learning?
- 10. Playing an Atari Game with Deep Recurrent Q-Networks
- 11. Policy-Based Methods for Reinforcement Learning
- 12. Evolutionary Strategies for RL 更新時間：2021-06-11 18:38:06
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