Open-source alternatives to Mujoco Py

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

This is the PyTorch implementation for inference and training of the world model and driving policy as described in:

We compare different Bayesian methods for representing an RL agent's uncertainty about cumulative rewards, including our own approach based on moment matching across the Bellman equations.

This package contains implementations of various RL algorithms for continuous control tasks simulated with MuJoCo.

Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning

This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error. The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients. The framewor

Deep Hierarchical Planning from Pixels

Code for the paper Learning Latent Dynamic Robust Representations for World Models (ICML-24).

This project is an AI research implementation library and machine learning research repository. It provides a collection of reference code, illustrative implementations, and open-source research datasets used to verify hypotheses and build upon existing models in artificial intelligence. The repository focuses on scientific research reproduction by translating theoretical findings from published papers into executable code. It includes specialized scientific simulation environments designed to test the behavior of autonomous agents and models within controlled settings. The project covers AI

bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent with two main objectives.

Simple and easily configurable grid world environments for reinforcement learning

Kinetics is a collection of large-scale, high-quality datasets of URL links of up to 650,000 video clips that cover 400/600/700 human action classes, depending on the dataset version. The videos include human-object interactions such as playing instruments, as well as human-human interactions…

PyBullet Gymperium

A collection of environments for autonomous driving and tactical decision-making tasks, developed and maintained by Edouard Leurent.

V-D4RL provides pixel-based analogues of the popular D4RL benchmarking tasks, derived from the dmcontrol suite, along with natural extensions of two state-of-the-art online pixel-based continuous control algorithms, DrQ-v2 and DreamerV2, to the offline setting. For further details, please see…

A collection of reference environments for offline reinforcement learning

COBS is an Off-Policy Policy Evaluation (OPE) Benchmarking Suite. The goal is to provide fine experimental control to carefully tease out an OPE method's performance across many key conditions.

Massively parallel rigidbody physics simulation on accelerator hardware.

robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

This repository contains an RL environment based on open-source game Gameplay Football. It was created by the Google Brain team for research purposes.

ROBEL is an open-source platform of cost-effective robots and associated reinforcement learning environments for benchmarking reinforcement learning in the real world. It provides Gym-compliant environments that easily run in both simulation (for rapid prototyping) and on real hardware. ROBEL…

A Multi-Task Reinforcement Learning Benchmark

This repo has moved to https://github.com/haosulab/ManiSkill. This repo now serves to ensure the old documentation link https://haosulab.github.io/ManiSkill2/ points to the new documentation

MiniHack is a sandbox framework for easily designing rich and diverse environments for Reinforcement Learning (RL). Based on the game of NetHack, MiniHack uses the NetHack Learning Environment (NLE) to communicate with the game and to provide a convenient interface for customly created RL…