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Status: Archive (code is provided as-is, no updates expected)
The main features of openai/multiagent-particle-envs are: Reinforcement Learning, Reinforcement Learning Environments.
Projects with overlapping indexed features include: dennybritz/reinforcement-learning — This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous… rlworkgroup/metaworld — Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning. deepmind/lab — Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using… deepmind/pysc2 — pysc2 is a Python interface and simulation framework that connects the StarCraft II game engine to machine learning… google/brax — Massively parallel rigidbody physics simulation on accelerator hardware. unity-technologies/ml-agents — This project is a reinforcement learning toolkit and simulation-based AI trainer for creating intelligent agents…
pysc2 is a Python interface and simulation framework that connects the StarCraft II game engine to machine learning agents. It acts as an API wrapper that exposes game internals as a set of observations and actions, providing a reinforcement learning environment for research and training. The framework includes tools for game replay analysis to extract data and sequences of actions from recorded matches for predictive modeling. It also provides an agent simulation environment to run and evaluate the performance of single or competing artificial intelligence agents. The system handles game ma
This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods. The project distinguishes itself by offering specialized frameworks for deep reinforcement learning and structured decision modeling. It includes implementations for deep Q-learning that utilize neural networks, experience replay, and prioritized sampling to approxima
Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using reinforcement learning. It serves as a spatial AI training simulator where agents can be evaluated through navigation and puzzle-solving tasks. The environment allows for the definition of complex layouts and task behaviors through external scripting, enabling the generation of specific challenges for AI research. It supports both automated training via standard API bindings and manual agent control to validate simulation dynamics. The system utilizes a grid-based spatial represen
Massively parallel rigidbody physics simulation on accelerator hardware.