AI4U is a plugin that allows you use the Godot Game Engine to specify agents with reinforcement learning. Non-Player Characters (NPCs) of games can be designed using ready-made components.

Reinforcement Learning through Tree-of-Thought (ToT) with pure math

FurnitureBench: Real-World Furniture Assembly Benchmark (RSS 2023)

Sotopia: an Open-ended Social Learning Environment (ICLR 2024 spotlight)

Procedural Environment Generation for Accelerated Multi-Agent Reinforcement Learning

The Core Reinforcement Learning library is intended to enable scalable deep reinforcement learning experimentation in a manner extensible to new simulations and new ways for the learning agents to interact with them. The hope is that this makes RL research easier by removing lock-in to particular simulations.The work is released under the follow…

Public release of Track-to-Learn: A general framework for tractography with deep reinforcement learning

Gym environment for building simulation and control using reinforcement learning

This repo contains a curative list of robot learning (mainly for manipulation) resources.

Reinforcement learning environments for planar robotics based on MuJoCo

C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

Simple Gridworld Gymnasium Environment

Realistic RL environments for vehicle fleets

Connect 4 (X) Environment + GYM + PyGame GUI

An easy-to-use and modular Python library for the Job Shop Scheduling Problem (JSSP)

Grid2Op a testbed platform to model sequential decision making in power systems.

C++ wrappers of OpenAI-Gym environments

Base of CraftGround, ⚡⚡ Lightning-Fast Minecraft Reinforcement Learning environment based on fabric

Extended, multi-agent and multi-objective (MaMoRL) environments based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.

This repository contains Dongming Shen's code and documentation for the research projects conducted at the AIDyS Lab, USC. The project focuses on integrating Reinforcement Learning (RL) to solve partially observable Markov decision processes (POMDP) under finite linear temporal logic (LTL) constraints.