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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PG on Pong

As covered in the previous section, the vanilla PG method works well on a simple CartPole environment, but surprisingly badly on more complicated environments. Even in the relatively simple Atari game Pong, our DQN was able to completely solve it in 1M frames and showed positive reward dynamics in just 100k frames, whereas PG failed to converge. Due to the instability of PG training, it became very hard to find good hyperparameters, which is still very sensitive to initialization.

This doesn’t mean that the PGs are bad, because, as we’ll see in the next chapter, just one tweak of the network architecture to get the better baseline in the gradients will turn PG into one of the best methods (Asynchronous Advantage Actor-Critic (A3C) method). Of course, there is a good chance that my hyperparameters are completely wrong or the code has some hidden bugs or whatever. Regardless, unsuccessful results still have value, at least as a demonstration of bad convergence dynamics. The complete...