Godot Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2112.03636v1
• Date: Tue, 7 Dec 2021 11:24:34 GMT
• Title: Godot Reinforcement Learning Agents
• Authors: Edward Beeching, Jilles Debangoye, Olivier Simonin, Christian Wolf
• Abstract summary: The Godot RL Agents interface allows the design, creation and learning of agent behaviors in challenging 2D and 3D environments. The framework is a versatile tool that allows researchers and game designers the ability to create environments with discrete, continuous and mixed action spaces.
• Score: 10.413185820687021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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