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 11 – Actor-Critic Methods – A2C and A3C

  1. The actor-critic method is one of the most popular algorithms in deep RL. Several modern deep RL algorithms are designed based on the actor-critic method. The actor-critic method lies at the intersection of value-based and policy-based methods. That is, it takes advantage of both value-based and policy-based methods.
  2. 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.
  3. In the policy gradient method with 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 network parameter at every step of the episode.
  4. In the actor network, we compute the gradient as .
  5. In advantage actor-critic (A2C), we compute the policy gradient with the advantage function and the advantage function...