Reinforcement Learning Environments for Omniverse Isaac Gym

A custom OpenFOAM solver with a temperature field added to the default simpleFoam solver.

[NeurIPS 2024] Goal Conditioned Reinforcement Learning for Photo Finishing Tuning

VTK-based Data Analysis and Visualization Application

✨ A modern Python package for interacting with OpenFOAM

Deep Reinforcement Learning

Lightweight version of MAPPO to help you quickly migrate to your local environment.

A collection of multi agent environments based on OpenAI gym.

Multi-Agent Reinforcement Learning with JAX

One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)

Modularized Implementation of Deep RL Algorithms in PyTorch

ChainerRL is a deep reinforcement learning library built on top of Chainer.

Deep Reinforcement Learning library for humans

A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

Run TensorFlow models in C++ without installation and without Bazel

A curated list of resources for using LLMs to develop more competitive grant applications.

A Python module implementing some standard algorithms used in nonlinear time series analysis

Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

Actor-Sharer-Learner training framework for off-policy DRL algorithms

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"

Toolbox to simulate GNF and viscoelastic fluid flows in OpenFOAM®

Code for the paper "Batch size invariance for policy optimization"

Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018)

[ICLR 2022] Accelerated Policy Learning with Parallel Differentiable Simulation

PyTorch implementation of soft actor critic

Implementations of selected inverse reinforcement learning algorithms.