mujoco

An elegant PyTorch deep reinforcement learning library.

Multi-Joint dynamics with Contact. A general purpose physics simulator.

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

OpenDILab Decision AI Engine

Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

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

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

Python library for Reinforcement Learning.

MyoSuite is a collection of environments/tasks to be solved by musculoskeletal models simulated with the MuJoCo physics engine and wrapped in the OpenAI gym API.

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

PyTorch implementation of Soft Actor-Critic (SAC)

DrQ: Data regularized Q

PyTorch implementation of Trust Region Policy Optimization

lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.

Trust Region Policy Optimization with TensorFlow and OpenAI Gym

OmniSafe is a comprehensive and reliable benchmark for safe reinforcement learning.

Real-time behaviour synthesis with MuJoCo, using Predictive Control

Reinforcement learning algorithms for MuJoCo tasks

DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)