Reinforcement Learning

Spis treści książki

• Preface
  • Objective
  • Who Should Read This Book?
  • Guiding Principles and Style
  • Prerequisites
  • Scope and Outline
  • Supplementary Materials
  • Conventions Used in This Book
    • Acronyms
    • Mathematical Notation
    • Fair Use Policy
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Why Reinforcement Learning?
  • Why Now?
  • Machine Learning
  • Reinforcement Learning
    • When Should You Use RL?
    • RL Applications
  • Taxonomy of RL Approaches
    • Model-Free or Model-Based
    • How Agents Use and Update Their Strategy
    • Discrete or Continuous Actions
    • Optimization Methods
    • Policy Evaluation and Improvement
  • Fundamental Concepts in Reinforcement Learning
    • The First RL Algorithm
      • Value estimation
      • Prediction error
      • Weight update rule
    • Is RL the Same as ML?
    • Reward and Feedback
      • Delayed rewards
      • Hindsight
  • Reinforcement Learning as a Discipline
  • Summary
  • Further Reading
• 2. Markov Decision Processes, Dynamic Programming, and Monte Carlo Methods
  • Multi-Arm Bandit Testing
    • Reward Engineering
    • Policy Evaluation: The Value Function
    • Policy Improvement: Choosing the Best Action
    • Simulating the Environment
    • Running the Experiment
    • Improving the -greedy Algorithm
  • Markov Decision Processes
    • Inventory Control
      • Transition table
      • Transition graph
      • Transition matrix
    • Inventory Control Simulation
  • Policies and Value Functions
    • Discounted Rewards
    • Predicting Rewards with the State-Value Function
      • Simulation using the state-value function
    • Predicting Rewards with the Action-Value Function
    • Optimal Policies
  • Monte Carlo Policy Generation
  • Value Iteration with Dynamic Programming
    • Implementing Value Iteration
    • Results of Value Iteration
  • Summary
  • Further Reading
• 3. Temporal-Difference Learning, Q-Learning, and n-Step Algorithms
  • Formulation of Temporal-Difference Learning
    • Q-Learning
    • SARSA
    • Q-Learning Versus SARSA
    • Case Study: Automatically Scaling Application Containers to Reduce Cost
  • Industrial Example: Real-Time Bidding in Advertising
    • Defining the MDP
    • Results of the Real-Time Bidding Environments
    • Further Improvements
  • Extensions to Q-Learning
    • Double Q-Learning
    • Delayed Q-Learning
    • Comparing Standard, Double, and Delayed Q-learning
    • Opposition Learning
  • n-Step Algorithms
    • n-Step Algorithms on Grid Environments
  • Eligibility Traces
  • Extensions to Eligibility Traces
    • Watkinss Q( )
    • Fuzzy Wipes in Watkinss Q( )
    • Speedy Q-Learning
    • Accumulating Versus Replacing Eligibility Traces
  • Summary
  • Further Reading
• 4. Deep Q-Networks
  • Deep Learning Architectures
    • Fundamentals
    • Common Neural Network Architectures
    • Deep Learning Frameworks
    • Deep Reinforcement Learning
  • Deep Q-Learning
    • Experience Replay
    • Q-Network Clones
    • Neural Network Architecture
    • Implementing DQN
    • Example: DQN on the CartPole Environment
      • Why train online?
      • Which is better? DQN versus Q-learning
    • Case Study: Reducing Energy Usage in Buildings
  • Rainbow DQN
    • Distributional RL
    • Prioritized Experience Replay
    • Noisy Nets
    • Dueling Networks
  • Example: Rainbow DQN on Atari Games
    • Results
    • Discussion
  • Other DQN Improvements
    • Improving Exploration
    • Improving Rewards
    • Learning from Offline Data
  • Summary
  • Further Reading
• 5. Policy Gradient Methods
  • Benefits of Learning a Policy Directly
  • How to Calculate the Gradient of a Policy
  • Policy Gradient Theorem
  • Policy Functions
    • Linear Policies
      • Logistic policy
      • Softmax policy
    • Arbitrary Policies
  • Basic Implementations
    • Monte Carlo (REINFORCE)
      • Example: REINFORCE on the CartPole environment
    • REINFORCE with Baseline
      • Example: REINFORCE with baseline on the CartPole environment
    • Gradient Variance Reduction
    • n-Step Actor-Critic and Advantage Actor-Critic (A2C)
      • Example: n-step actor-critic on the CartPole environment
      • State-value learning decay rates versus policy decay rates
    • Eligibility Traces Actor-Critic
      • Example: Eligibility trace actor-critic on the CartPole environment
    • A Comparison of Basic Policy Gradient Algorithms
  • Industrial Example: Automatically Purchasing Products for Customers
    • The Environment: Gym-Shopping-Cart
    • Expectations
    • Results from the Shopping Cart Environment
  • Summary
  • Further Reading
• 6. Beyond Policy Gradients
  • Off-Policy Algorithms
    • Importance Sampling
    • Behavior and Target Policies
    • Off-Policy Q-Learning
    • Gradient Temporal-Difference Learning
    • Greedy-GQ
    • Off-Policy Actor-Critics
  • Deterministic Policy Gradients
    • Deterministic Policy Gradients
    • Deep Deterministic Policy Gradients
      • DDPG derivation
      • DDPG implementation
    • Twin Delayed DDPG
      • Delayed policy updates (DPU)
      • Clipped double Q-learning (CDQ)
      • Target policy smoothing (TPS)
      • TD3 implementation
    • Case Study: Recommendations Using Reviews
    • Improvements to DPG
  • Trust Region Methods
    • KullbackLeibler Divergence
      • KL divergence experiments
    • Natural Policy Gradients and Trust Region Policy Optimization
    • Proximal Policy Optimization
      • PPOs clipped objective
      • PPOs value function and exploration objectives
  • Example: Using Servos for a Real-Life Reacher
    • Experiment Setup
    • RL Algorithm Implementation
    • Increasing the Complexity of the Algorithm
    • Hyperparameter Tuning in a Simulation
    • Resulting Policies
  • Other Policy Gradient Algorithms
    • Retrace( )
    • Actor-Critic with Experience Replay (ACER)
    • Actor-Critic Using Kronecker-Factored Trust Regions (ACKTR)
    • Emphatic Methods
  • Extensions to Policy Gradient Algorithms
    • Quantile Regression in Policy Gradient Algorithms
  • Summary
    • Which Algorithm Should I Use?
    • A Note on Asynchronous Methods
  • Further Reading
• 7. Learning All Possible Policies with Entropy Methods
  • What Is Entropy?
  • Maximum Entropy Reinforcement Learning
  • Soft Actor-Critic
    • SAC Implementation Details and Discrete Action Spaces
    • Automatically Adjusting Temperature
    • Case Study: Automated Traffic Management to Reduce Queuing
  • Extensions to Maximum Entropy Methods
    • Other Measures of Entropy (and Ensembles)
    • Optimistic Exploration Using the Upper Bound of Double Q-Learning
    • Tinkering with Experience Replay
    • Soft Policy Gradient
    • Soft Q-Learning (and Derivatives)
    • Path Consistency Learning
  • Performance Comparison: SAC Versus PPO
  • How Does Entropy Encourage Exploration?
    • How Does the Temperature Parameter Alter Exploration?
  • Industrial Example: Learning to Drive with a Remote Control Car
    • Description of the Problem
    • Minimizing Training Time
    • Dramatic Actions
    • Hyperparameter Search
    • Final Policy
    • Further Improvements
  • Summary
    • Equivalence Between Policy Gradients and Soft Q-Learning
    • What Does This Mean For the Future?
    • What Does This Mean Now?
• 8. Improving How an Agent Learns
  • Rethinking the MDP
    • Partially Observable Markov Decision Process
      • Predicting the belief state
    • Case Study: Using POMDPs in Autonomous Vehicles
    • Contextual Markov Decision Processes
    • MDPs with Changing Actions
    • Regularized MDPs
  • Hierarchical Reinforcement Learning
    • Naive HRL
    • High-Low Hierarchies with Intrinsic Rewards (HIRO)
    • Learning Skills and Unsupervised RL
    • Using Skills in HRL
    • HRL Conclusions
  • Multi-Agent Reinforcement Learning
    • MARL Frameworks
    • Centralized or Decentralized
    • Single-Agent Algorithms
    • Case Study: Using Single-Agent Decentralized Learning in UAVs
    • Centralized Learning, Decentralized Execution
    • Decentralized Learning
    • Other Combinations
    • Challenges of MARL
    • MARL Conclusions
  • Expert Guidance
    • Behavior Cloning
    • Imitation RL
    • Inverse RL
    • Curriculum Learning
  • Other Paradigms
    • Meta-Learning
    • Transfer Learning
  • Summary
  • Further Reading
• 9. Practical Reinforcement Learning
  • The RL Project Life Cycle
    • Life Cycle Definition
      • Data science life cycle
      • Reinforcement learning life cycle
  • Problem Definition: What Is an RL Project?
    • RL Problems Are Sequential
    • RL Problems Are Strategic
    • Low-Level RL Indicators
      • An entity
      • An environment
      • A state
      • An action
      • Quantify success or failure
    • Types of Learning
      • Online learning
      • Offline or batch learning
      • Concurrent learning
      • Reset-free learning
  • RL Engineering and Refinement
    • Process
    • Environment Engineering
      • Implementation
      • Simulation
      • Interacting with real life
    • State Engineering or State Representation Learning
      • Learning forward models
      • Constraints
      • Transformation (dimensionality reduction, autoencoders, and world models)
    • Policy Engineering
      • Discrete states
      • Continuous states
      • Converting to discrete states
      • Mixed state spaces
    • Mapping Policies to Action Spaces
      • Binary actions
      • Continuous actions
      • Hybrid action spaces
      • When to perform actions
      • Massive action spaces
    • Exploration
      • Is intrinsic motivation exploration?
      • Visitation counts (sampling)
      • Information gain (surprise)
      • State prediction (curiosity or self-reflection)
      • Curious challenges
      • Random embeddings (random distillation networks)
      • Distance to novelty (episodic curiosity)
      • Exploration conclusions
    • Reward Engineering
      • Reward engineering guidelines
      • Reward shaping
      • Common rewards
      • Reward conclusions
  • Summary
  • Further Reading
• 10. Operational Reinforcement Learning
  • Implementation
    • Frameworks
      • RL frameworks
      • Other frameworks
    • Scaling RL
      • Distributed training (Gorila)
      • Single-machine training (A3C, PAAC)
      • Distributed replay (Ape-X)
      • Synchronous distribution (DD-PPO)
      • Improving utilization (IMPALA, SEED)
      • Scaling conclusions
    • Evaluation
      • Policy performance measures
      • Statistical policy comparisons
      • Algorithm performance measures
      • Problem-specific performance measures
      • Explainability
      • Evaluation conclusions
  • Deployment
    • Goals
      • Goals during different phases of development
      • Best practices
      • Hierarchy of needs
    • Architecture
    • Ancillary Tooling
      • Build versus buy
      • Monitoring
      • Logging and tracing
      • Continuous integration and continuous delivery
      • Experiment tracking
      • Hyperparameter tuning
      • Deploying multiple agents
      • Deploying policies
    • Safety, Security, and Ethics
      • Safe RL
      • Secure RL
      • Ethical RL
  • Summary
  • Further Reading
• 11. Conclusions and the Future
  • Tips and Tricks
    • Framing the Problem
    • Your Data
    • Training
    • Evaluation
    • Deployment
  • Debugging
    • ${ALGORITHM_NAME} Cant Solve ${ENVIRONMENT}!
    • Monitoring for Debugging
  • The Future of Reinforcement Learning
    • RL Market Opportunities
    • Future RL and Research Directions
      • Research in industry
      • Research in academia
      • Ethical standards
  • Concluding Remarks
    • Next Steps
    • Now Its Your Turn
  • Further Reading
• A. The Gradient of a Logistic Policy for Two Actions
• B. The Gradient of a Softmax Policy
• Glossary
  • Acronyms and Common Terms
  • Symbols and Notation
• Index

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