We covered the terms on- and off-policy previously when we looked at MC training in Chapter 2, Monte Carlo Methods. Recall that the agent didn't update its policy until after an episode. Hence, this defines the TD(0) method of learning in the last example as an off-policy learner. In our last example, it may seem that the agent is learning online but it still, in fact, trains a policy or Q table externally. That is, the agent needs to build up a policy before it can learn to make decisions and play the game. Ideally, we want our agent to learn or improve its policy as it plays through an episode. After all, we don't learn offline nor does any other biological animal. Instead, our goal will be to understand how an agent can learn using on-policy learning. On-policy learning will be covered in Chapter 5, Exploring SARSA.
Hands-On Reinforcement Learning for Games
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Hands-On Reinforcement Learning for Games
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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.
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Current Title:
Hands-On Reinforcement Learning for Games
Table of Contents (19 chapters)
Preface
Section 1: Exploring the Environment
Free Chapter
Understanding Rewards-Based Learning
Dynamic Programming and the Bellman Equation
Monte Carlo Methods
Temporal Difference Learning
Exploring SARSA
Section 2: Exploiting the Knowledge
Going Deep with DQN
Going Deeper with DDQN
Policy Gradient Methods
Optimizing for Continuous Control
All about Rainbow DQN
Exploiting ML-Agents
DRL Frameworks
Section 3: Reward Yourself
3D Worlds
From DRL to AGI
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