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Part II: Reinforcement Learning Basics.md

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Part II: Reinforcement Learning Basics

Chapter 3: Fundamentals of Reinforcement Learning

  • Introduction to Reinforcement Learning: Provides an overview of reinforcement learning (RL), explaining its principles and basic concepts.
  • Key Components of RL: Explores the key components of RL, including agents, environments, states, actions, rewards, and policies.
  • Markov Decision Processes (MDPs): Introduces MDPs as the mathematical framework for modeling RL problems, discussing states, actions, transition probabilities, and rewards.
  • Exploration vs. Exploitation: Discusses the trade-off between exploration and exploitation in RL algorithms and their impact on learning.

Chapter 4: Deep Reinforcement Learning

  • Introduction to Deep RL: Explores the extension of RL with deep learning techniques, highlighting the advantages and challenges of deep RL.
  • Deep Q-Networks (DQN): Introduces DQN as a foundational deep RL algorithm, discussing its architecture, training process, and applications.
  • Policy Gradient Methods: Discusses policy gradient methods as an alternative approach to value-based methods, focusing on algorithms like REINFORCE and PPO.
  • Actor-Critic Methods: Explores actor-critic methods that combine aspects of both value-based and policy gradient approaches, including algorithms like A2C and A3C.

Chapter 5: Reinforcement Learning in AWS DeepRacer

  • Adapting RL to Autonomous Racing: Discusses the application of RL techniques in the context of autonomous racing, highlighting challenges and opportunities.
  • Simulation Environment: Explores the simulation environment provided by AWS DeepRacer, including track design, sensor emulation, and physics simulation.
  • Reward Function Design: Discusses the crucial role of reward functions in training RL agents for autonomous racing, emphasizing design principles and best practices.
  • Hyperparameter Optimization: Introduces techniques for hyperparameter optimization in RL algorithms, including grid search, random search, and Bayesian optimization.

By the conclusion of Part II, readers gain a solid understanding of the fundamental concepts and techniques of reinforcement learning, paving the way for deeper exploration into their application in autonomous racing with AWS DeepRacer.