Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API.
This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem.
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总体上就是介绍Unity ML-Agents v0.3版本的示例，但是行文缺乏逻辑，调参部分也没有给出可行建议，价值不太大。
评分总体上就是介绍Unity ML-Agents v0.3版本的示例，但是行文缺乏逻辑，调参部分也没有给出可行建议，价值不太大。
评分总体上就是介绍Unity ML-Agents v0.3版本的示例，但是行文缺乏逻辑，调参部分也没有给出可行建议，价值不太大。
评分总体上就是介绍Unity ML-Agents v0.3版本的示例，但是行文缺乏逻辑，调参部分也没有给出可行建议，价值不太大。
评分总体上就是介绍Unity ML-Agents v0.3版本的示例，但是行文缺乏逻辑，调参部分也没有给出可行建议，价值不太大。
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