google/brax
Brax
Massively parallel rigidbody physics simulation on accelerator hardware.
Features
- Reinforcement Learning - Differentiable physics engine for training reinforcement learning agents.
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Massively parallel rigidbody physics simulation on accelerator hardware.
The main features of google/brax are: Reinforcement Learning, Reinforcement Learning Environments, Physics Simulation.
Projects with overlapping indexed features include: openai/multiagent-particle-envs — Status: Archive (code is provided as-is, no updates expected). rlworkgroup/metaworld — Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning. deepmind/pysc2 — pysc2 is a Python interface and simulation framework that connects the StarCraft II game engine to machine learning… deepmind/lab — Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using… dennybritz/reinforcement-learning — This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous… unity-technologies/ml-agents — This project is a reinforcement learning toolkit and simulation-based AI trainer for creating intelligent agents…
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
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
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
Status: Archive (code is provided as-is, no updates expected)