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)
Other Books You May Enjoy


We started the chapter by understanding what the actor-critic method is. We learned that in the actor-critic method, the actor computes the optimal policy, and the critic evaluates the policy computed by the actor network by estimating the value function. Next, we learned how the actor-critic method differs from the policy gradient method with the baseline.

We learned that in the policy gradient method with the baseline, first, we generate complete episodes (trajectories), and then we update the parameter of the network. Whereas, in the actor-critic method, we update the parameter of the network at every step of the episode. Moving forward, we learned what the advantage actor-critic algorithm is and how it uses the advantage function in the gradient update.

At the end of the chapter, we learned about another interesting actor-critic algorithm, called asynchronous advantage actor-critic method. We learned that A3C consists of several worker agents and...