Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
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27
Index

The PTAN CartPole solver

Let's now take the PTAN classes (without Ignite so far) and try to combine everything together to solve our first environment: CartPole. The complete code is in Chapter07/06_cartpole.py. I will show only the important parts of the code related to the material that we have just covered.

net = Net(obs_size, HIDDEN_SIZE, n_actions)
tgt_net = ptan.agent.TargetNet(net)
selector = ptan.actions.ArgmaxActionSelector()
selector = ptan.actions.EpsilonGreedyActionSelector(
    epsilon=1, selector=selector)
agent = ptan.agent.DQNAgent(net, selector)
exp_source = ptan.experience.ExperienceSourceFirstLast(
    env, agent, gamma=GAMMA)
buffer = ptan.experience.ExperienceReplayBuffer(
    exp_source, buffer_size=REPLAY_SIZE)

In the beginning, we create the NN (the simple two-layer feed-forward NN that we used for CartPole before) and target the NN epsilon-greedy action selector and DQNAgent. Then the experience source and replay...