We present Godot Reinforcement Learning (RL) Agents, an open-source interface for developing environments and agents in the Godot Game Engine. The Godot RL Agents interface allows the design, creation and learning of agent behaviors in challenging 2D and 3D environments with various on-policy and off-policy Deep RL algorithms. We provide a standard Gym interface, with wrappers for learning in the Ray RLlib and Stable Baselines RL frameworks. This allows users access to over 20 state of the art on-policy, off-policy and multi-agent RL algorithms. The framework is a versatile tool that allows researchers and game designers the ability to create environments with discrete, continuous and mixed action spaces. The interface is relatively performant, with 12k interactions per second on a high end laptop computer, when parallized on 4 CPU cores. An overview video is available here: https://youtu.be/g1MlZSFqIj4
翻译:我们展示了Godot加强学习(RL)代理器,这是Godot游戏引擎中开发环境和代理器的开放源界面。Godot RL代理器界面允许设计、创建和学习2D和3D环境的代理器行为,与各种政策和非政策深度深度RL算法一起挑战2D和3D环境。我们提供了一个标准的 Gym 界面,在Ray RLlib和稳定基线RL框架中提供包装软件,供学习。这允许用户访问超过20种关于政策、非政策和多试RL算法的艺术状态。这个框架是一个多功能工具,使研究人员和游戏设计者能够以离散、连续和混合的动作空间创造环境。这个界面相对较有性,高端笔记本电脑每秒有12k互动,在4个CPU核心上投影。一个概览视频可在这里查阅:https://youtu.be/g1MLZSFQj4。