Open-source alternatives to D4rl

Python package providing easy to use Gym environments and data access for training agents in Minecraft.

Gym is a reinforcement learning environment toolkit and agent simulation framework. It provides a standardized API and a universal communication interface that defines how learning agents interact with simulation environments through actions and observations. The project includes a benchmark environment suite and a diverse library of pre-configured simulation worlds, including physics engines and classic control tasks. It enables the creation of custom simulation environments to train agents in specific operational scenarios while ensuring reproducibility across different learning algorithms.

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

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

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

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

RL4RS is a real-world deep reinforcement learning recommender system benchmark for practitioners and researchers.

Deep Hierarchical Planning from Pixels

RLDS stands for Reinforcement Learning Datasets and it is an ecosystem of tools to store, retrieve and manipulate episodic data in the context of Sequential Decision Making including Reinforcement Learning (RL), Learning for Demonstrations, Offline RL or Imitation Learning.

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

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

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…

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

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.

robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

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

Massively parallel rigidbody physics simulation on accelerator hardware.

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

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

This is an original PyTorch implementation of the ExORL framework from

Official code for the "Towards Evaluating Adaptivity of Model-Based Reinforcement Learning" paper.