Open-source alternatives to Spitis Mrl

Design-Bench is a benchmarking framework for solving automatic design problems that involve choosing an input that maximizes a black-box function. This type of optimization is used across scientific and engineering disciplines in ways such as designing proteins and DNA sequences with particular…

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

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.

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…

This is the code used for the paper:

Status: Stable release

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

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

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

Implementation of distributed RL algorithms:

Jason Yecheng Ma 12 , Shagun Sodhani 1 Dinesh Jayaraman 2 , Osbert Bastani 2 , {Vikash Kumar 1 , Amy Zhang 1 }

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

Massively parallel rigidbody physics simulation on accelerator hardware.

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…

SMARTS (Scalable Multi-Agent Reinforcement Learning Training School) is a simulation platform for multi-agent reinforcement learning (RL) and research on autonomous driving. Its focus is on realistic and diverse interactions. It is part of the XingTian suite of RL platforms from Huawei Noah's…

Scalable, event-driven, deep-learning-friendly backtesting library

Trading environnement for RL agents, backtesting and training.

This repository contains the implementation for the AoS paper "Testing Stationarity and Change Point Detection in Reinforcement Learning" in Python (and R for plotting).

A simple 2D maze environment where an agent (blue dot) finds its way from the top left corner (blue square) to the goal at the bottom right corner (red square). The objective is to find the shortest path from the start to the goal.

Simple and easily configurable grid world environments for reinforcement learning

Miniworld is being maintained by the Farama Foundation (https://farama.org/project_standards). See the Project Roadmap for details regarding the long-term plans.