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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Connect4 results

To make the training fast, the hyperparameters of the training process were intentionally chosen to be small. For example, at every step of the self-play process, only 10 MCTS were performed, each with a minibatch size of eight. This, in combination with efficient minibatch MCTS and the fast game engine, made training very fast. Basically, after just one hour of training and 2,500 games played in the self-play mode, the produced model was sophisticated enough to be enjoyable to play against. Of course, the level of its play was well below even a kid's level, but it showed some rudimentary strategies and made mistakes in only every other move, which was good progress.

The training was left running for a day, which resulted in 55k games played by a best model and, in total, 102 best model rotations. The training dynamics are shown in the following charts:

Figure 3: Training convergence

The tournament verification was complicated by the number of different models, as several games...