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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

Advanced Reinforcement Learning Techniques

What You'll Learn

Deep Q-Network Variants
Implement and understand algorithms like DQN, Double DQN, Dueling DQN, and Prioritized Experience Replay.
Advanced Policy Gradient Methods
Grasp the theory and application of A2C, A3C, DDPG, TRPO, and PPO.
Sophisticated Exploration Strategies
Apply advanced exploration techniques beyond epsilon-greedy, including curiosity-driven and count-based methods.
Model-Based Reinforcement Learning
Develop agents that learn environment models and use them for planning (e.g., Dyna-Q, MCTS integration concepts).
Multi-Agent Reinforcement Learning
Understand the challenges and implement algorithms for cooperative and competitive multi-agent scenarios (e.g., MADDPG).
Offline Reinforcement Learning
Learn techniques to train RL agents from fixed datasets, addressing challenges like distributional shift.
Implementation and Optimization
Optimize deep RL implementations, tune hyperparameters, and debug agent behavior effectively.

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