Advanced Reinforcement Learning Techniques
Chapter 1: Foundations Revisited and Function Approximation
Markov Decision Processes Formulation Review
Bellman Equations and Optimality Conditions
Value Iteration and Policy Iteration
Temporal Difference Learning Methods
Introduction to Policy Gradient Methods
Function Approximation in Reinforcement Learning
The Deadly Triad in Off-Policy Learning
Chapter 2: Deep Q-Networks and Enhancements
Limitations of Linear Function Approximation
Deep Q-Networks (DQN) Algorithm
Experience Replay Mechanism
Target Networks for Training Stability
Double Deep Q-Networks (DDQN)
Dueling Network Architectures
Prioritized Experience Replay (PER)
Distributional Reinforcement Learning Concepts
Rainbow DQN Integration
DQN Variants Implementation Hands-on Practical
Chapter 3: Advanced Policy Gradient and Actor-Critic Methods
Challenges in Basic Policy Gradients
Actor-Critic Architecture Fundamentals
Baselines for Variance Reduction
Advantage Actor-Critic (A2C) and A3C
Generalized Advantage Estimation (GAE)
Deep Deterministic Policy Gradient (DDPG)
Trust Region Policy Optimization (TRPO)
Proximal Policy Optimization (PPO)
Soft Actor-Critic (SAC)
Actor-Critic Methods Implementation Practice
Chapter 4: Advanced Exploration Strategies
The Exploration-Exploitation Trade-off Revisited
Optimism in the Face of Uncertainty: UCB Methods
Probability Matching: Thompson Sampling
Parameter Space Noise for Exploration
Pseudo-Counts: Count-Based Exploration
Prediction Error as Curiosity: Intrinsic Motivation
State Novelty: Random Network Distillation (RND)
Information Gain for Exploration
Comparing and Combining Exploration Techniques
Exploration Strategy Implementation Practice
Chapter 5: Model-Based Reinforcement Learning
Rationale for Model-Based RL
Taxonomy of Model-Based Methods
Learning Environment Dynamics Models
Dyna Architectures: Integrating Learning and Planning
Planning with Learned Models: Trajectory Sampling
Monte Carlo Tree Search (MCTS) Fundamentals
Integrating MCTS with Learned Models
Model Predictive Control (MPC) Connections
Challenges: Model Accuracy and Computational Cost
Simple Model-Based Agent Hands-on Practical
Chapter 6: Multi-Agent Reinforcement Learning
Introduction to Multi-Agent Systems
MARL Problem Formulation: Stochastic Games
Centralized vs Decentralized Control
Challenge: The Non-Stationarity Problem
Independent Learners (IQL, IDDPG)
Parameter Sharing Strategies
Centralized Training with Decentralized Execution (CTDE)
Value Decomposition Methods (VDN, QMIX)
Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
Communication Protocols in MARL
MARL Implementation Practice
Chapter 7: Offline Reinforcement Learning
Introduction to Offline RL (Batch RL)
Differences from Online and Off-Policy RL
Challenge: Distributional Shift
Off-Policy Evaluation in the Offline Setting
Importance Sampling and its Limitations
Fitted Q-Iteration (FQI) Approaches
Policy Constraint Methods
Batch-Constrained Deep Q-learning (BCQ)
Value Regularization Methods
Conservative Q-Learning (CQL)
Offline RL Implementation Considerations
Offline RL Algorithm Practice
Chapter 8: Implementation Details and Optimization
Neural Network Architectures for RL
Hyperparameter Tuning Strategies
Action and Observation Space Representation
Code Structuring for RL Projects
Software Frameworks and Libraries
Distributed Reinforcement Learning Approaches
Reproducibility in Deep RL
Debugging and Visualization Techniques
Performance Optimization and Hardware Considerations
Agent Debugging and Tuning Practice
Offline Reinforcement Learning
Previous chapters concentrated on reinforcement learning agents that improve through active interaction with an environment. This chapter shifts to Offline Reinforcement Learning, often called Batch RL. Here, the challenge is to learn effective policies solely from a static, pre-collected dataset of transitions, without any opportunity for further environment interaction.
We will begin by outlining the motivations for offline learning and highlighting its key differences from the online and off-policy settings discussed earlier. A central difficulty in Offline RL is distributional shift: the mismatch between the distribution of states and actions in the fixed dataset (generated by some behavior policy πb) and the distributions induced by the new policies being evaluated or learned.
This chapter will cover:
- The concept of distributional shift and its implications.
- Methods for Offline Off-Policy Evaluation (OPE), including Importance Sampling and its practical challenges.
- Algorithms specifically designed to mitigate distributional shift, such as policy constraint methods (e.g., BCQ) that restrict the learned policy to actions supported by the data.
- Value regularization techniques (e.g., CQL) that penalize unrealistic Q-values for actions not present in the dataset.
- Practical implementation details relevant to the offline setting.
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