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 CartPole

Nowadays, almost nobody uses the vanilla PG method, as the much more stable Actor-Critic method exists, which will be the topic of the two following chapters. However, I still want to show the PG implementation, as it establishes very important concepts and metrics to check for the PG method’s performance. So, we will start with a much simpler environment of CartPole, and in the next section, will check its performance on our favorite Pong environment. The complete code for the following example is available in Chapter09/

GAMMA = 0.99

Besides already familiar hyperparameters, we have two new ones. Entropy beta value is the scale of the entropy bonus. The REWARD_STEPS value specifies how many steps ahead the Bellman equation is unrolled to estimate the discounted total reward of every transition.

class PGN(nn.Module):
    def __init__(self, input_size, n_actions):
        super(PGN, self...