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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Variance reduction methods

In the previous section, we learned one of the simplest policy gradient methods, called the REINFORCE method. One major issue we face with the policy gradient method we learned in the previous section is that the gradient, , will have high variance in each update. The high variance is basically due to the major difference in the episodic returns. That is, we learned that policy gradient is the on-policy method, which means that we improve the same policy with which we are generating episodes in every iteration. Since the policy is getting improved on every iteration, our return varies greatly in each episode and it introduces a high variance in the gradient updates. When the gradients have high variance, then it will take a lot of time to attain convergence.

Thus, now we will learn the following two important methods to reduce the variance:

  • Policy gradients with reward-to-go (causality)
  • Policy gradients with baseline