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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Cross-entropy on CartPole

The whole code for this example is in Chapter04/, but the following are the most important parts. Our model's core is a one-hidden-layer neural network, with ReLU and 128 hidden neurons (which is absolutely arbitrary). Other hyperparameters are also set almost randomly and aren't tuned, as the method is robust and converges very quickly.


We define constants at the top of the file and they include the count of neurons in the hidden layer, the count of episodes we play on every iteration (16), and the percentile of episodes' total rewards that we use for elite episode filtering. We'll take the 70th percentile, which means that we'll leave the top 30% of episodes sorted by reward:

class Net(nn.Module):
    def __init__(self, obs_size, hidden_size, n_actions):
        super(Net, self).__init__() = nn.Sequential(
            nn.Linear(obs_size, hidden_size),