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
Information Gain for Exploration
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- Variational Information Maximizing Exploration, Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel, 2016 Advances in Neural Information Processing Systems, Vol. 29 (Curran Associates, Inc.) DOI: 10.48550/arXiv.1605.09674 - Presents Variational Information Maximizing Exploration (VIME), a deep reinforcement learning method that directly maximizes information gain about the environment's dynamics using variational inference for exploration.
- Deep Exploration via Bootstrapped DQN, Ian Osband, Charles Blundell, Alexander Pritzel, Benjamin Van Roy, 2016 Advances in Neural Information Processing Systems 29 (Neural Information Processing Systems) - Describes Deep Exploration via Bootstrapped DQN, an exploration strategy that uses an ensemble of value functions to represent model uncertainty and direct exploration, acting as a proxy for information gain.
- Bayesian Reinforcement Learning: A Survey, Mohammad Ghavamzadeh, Shie Mannor, Joelle Pineau, and Aviv Tamar, 2015 Foundations and Trends in Machine Learning, Vol. 8 (Now Publishers) DOI: 10.1561/2200000049 - Offers a thorough review of Bayesian reinforcement learning, explaining how probabilistic models and belief distributions are used to handle uncertainty, a core element of information gain exploration.
- PILCO: A Model-Based Reinforcement Learning Architecture for Fast and Robust Policy Search, Marc Peter Deisenroth and Carl Edward Rasmussen, 2011 Proceedings of the 28th International Conference on Machine Learning (ICML), Vol. 15 (PMLR (Proceedings of Machine Learning Research)) DOI: 10.5592/mlr.2011.v15.deisenroth11a - Presents PILCO, a model-based reinforcement learning algorithm using Gaussian processes to maintain a probabilistic dynamics model. This method tracks model uncertainty, which guides effective exploration by focusing on areas with less understanding, and assists robust policy optimization.