Book Image

Deep Reinforcement Learning with Python - Second Edition

By : Sudharsan Ravichandiran
Book Image

Deep Reinforcement Learning with Python - Second Edition

By: Sudharsan Ravichandiran

Overview of this book

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.
Table of Contents (22 chapters)
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Chapter 3 – The Bellman Equation and Dynamic Programming

  1. The Bellman equation states that the value of a state can be obtained as a sum of the immediate reward and the discounted value of the next state. Similar to the Bellman equation of the value function, the Bellman equation of the Q function states that the Q value of a state-action pair can be obtained as a sum of the immediate reward and the discounted Q value of the next state-action pair.
  2. The Bellman expectation equation gives the Bellman value and Q functions whereas the Bellman optimality equation gives the optimal Bellman value and Q functions.
  3. The value function can be derived from the Q function as .
  4. The Q function can be derived from the value function as .
  5. In the value iteration method, we perform the following steps:
    1. Compute the optimal value function by taking maximum over Q function, that is,
    2. Extract the optimal policy from the computed...