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)
18
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19
Index

TD control

In the control method, our goal is to find the optimal policy, so we will start off with an initial random policy and then we will try to find the optimal policy iteratively. In the previous chapter, we learned that the control method can be classified into two categories:

  • On-policy control
  • Off-policy control

We learned what on-policy and off-policy control means in the previous chapter. Let's recap that a bit before going ahead. In the on-policy control, the agent behaves using one policy and tries to improve the same policy. That is, in the on-policy method, we generate episodes using one policy and improve the same policy iteratively to find the optimal policy. In the off-policy control method, the agent behaves using one policy and tries to improve a different policy. That is, in the off-policy method, we generate episodes using one policy and we try to improve a different policy iteratively to find the optimal policy...