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Code for a multi-agent particle environment used in a paper
Multiagent Particle Environments is a lightweight framework for simulating multi-agent reinforcement learning tasks in a continuous observation space with discrete action settings. It was originally developed by OpenAI and used in the influential paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The environment provides simple particle-based worlds with simulated physics, where agents can move, communicate, and interact with each other. Scenarios are designed to...
A library for implementing GOAP in an AI agent.
Based on pygoap v3 by Leif Theden et al.
Updated code to work without having pygame installed, bug-fixed functions to make them implement the behaviors that were expected, and implemented desired behaviors so that the Pirate demo works properly for all known actions and goals.