Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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ES on HalfCheetah

In the next example, we'll go beyond the simplest ES implementation and look at how this method can be parallelized efficiently using the shared seed strategy proposed by the paper [1]. To show this approach, we'll use the environment from the roboschool library that we already experimented with in Chapter 15, Trust Regions – TRPO, PPO, and ACKTR, HalfCheetah, which is a continuous action problem where a weird two-legged creature gains reward by running forward without injuring itself.

First, let's discuss the idea of shared seeds. The performance of the ES algorithm is mostly determined by the speed that we can gather our training batch, which consists of sampling the noise and checking the total reward of the perturbed noise. As our training batch items are independent, we can easily parallelize this step to a large number of workers sitting on remote machines (that's a bit similar to the example from Chapter 11, Asynchronous Advantage Actor-Critic when we gathered gradients...