Deep Reinforcement Learning Foundations
A deep dive into neural-powered RL with DQN, PPO, and SAC, unlocking solutions for complex, real-world problems.
Chapter 12: Neural Networks for RL (Function approximation, backpropagation, stability in RL) Chapter 13: Deep Q-Networks (DQN) DQN: Experience replay, target networks Variants: Double DQN, C51, QRDQN, Rainbow, IQN, FQF Advanced Q-learning: SQL, SQN, MDQN, Averaged-DQN References Chapter 14: Deep Policy Gradient Methods A2C, A3C, TRPO, PPO, PPG DDPG, ACER, IMPALA SAC: Soft Actor-Critic References Chapter 15: Value Function Approximation (Deep SARSA, fitted Q-iteration, distributional RL) Chapter 16: Training Stability and Optimization (Reward clipping, normalization, clipped objectives, entropy regularization)