Book Image

Hands-On Reinforcement Learning for Games

By : Micheal Lanham
Book Image

Hands-On Reinforcement Learning for Games

By: Micheal Lanham

Overview of this book

With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications.
Table of Contents (19 chapters)
1
Section 1: Exploring the Environment
7
Section 2: Exploiting the Knowledge
15
Section 3: Reward Yourself

Using prediction and control

When we previously had a model, our algorithm could learn to plan and improve a policy offline. Now, with no model, our algorithm needs to become an agent and learn to explore and, while doing that, also learn and improve. This allows our agent to now learn effectively by trial and error. Let's jump back into the Chapter_3_3.py code example and follow the exercise:

  1. We will start right from where we left off and review the last couple of lines including the play_game function:
episode = play_game(env=env, policy=policy, display=False)
evaluate_policy_check(env, e, policy, test_policy_freq)
  1. Inside evaluate_policy_check, we test to see whether the test_policy_freq number has been reached. If it has, we output the current progress of the agent. In reality, what we are evaluating is how well the current policy will run an agent. The evaluate_policy_check...