Deep Reinforcement Learning with Python: Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

Explores various aspects of reinforcement learning (RL) and deep reinforcement learning (DRL), covering both foundational concepts and advanced algorithms. Authored by Sudharsan Ravichandiran, a data scientist and researcher, the book explains core RL ideas such as rewards, policies, Markov Decision Processes (MDPs), and value functions, using practical examples like the Frozen Lake and Atari environments. It progresses into deep learning (DL) fundamentals, including neural networks, activation functions, and optimization algorithms like gradient descent. The book then details prominent DRL algorithms, including Deep Q Networks (DQN) and its variants, policy gradient methods (REINFORCE, Actor-Critic, A2C, A3C, DDPG, TD3, SAC, TRPO, PPO, ACKTR), and distributional reinforcement learning approaches (Categorical DQN, QR-DQN, D4PG). Finally, it introduces emerging RL frontiers such as meta-RL, hierarchical RL, and imitation learning, often illustrating concepts with Python code and TensorFlow.

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Deep Reinforcement Learning with Python: Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

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