32 Open source projects that are alternatives of or similar to Metaworld

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

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

Trust Region Policy Optimization with TensorFlow and OpenAI Gym

PyTorch implementation of Trust Region Policy Optimization

DrQ: Data regularized Q

Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient - 7th ICML AutoML workshop 2020

Walkthroughs for DSL, AirSim, the Vector Institute, and more

Simple kinematics calculation toolkit for robotics

Trajectory optimization algorithms for robotic control.

A Contact-Implicit Trajectory Optimization Package

A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.

Deep Developmental Reinforcement Learning

Python wrapper for MuJoCo physics simulation.

Solutions of assignments of Deep Reinforcement Learning course presented by the University of California, Berkeley (CS285) in Pytorch framework

Proto-RL: Reinforcement Learning with Prototypical Representations

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

Mujoco Model for UR5-Ridgeback-Robotiq Robot

A Repository with C++ implementations of Reinforcement Learning Algorithms (Pytorch)

JAX implementations of core Deep RL algorithms

ROS integration of Mujoco simulator

Provide full reinforcement learning benchmark on mujoco environments, including ddpg, sac, td3, pg, a2c, ppo, library

An elegant PyTorch deep reinforcement learning library.

PyTorch Implementation of REINFORCE for both discrete & continuous control

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.

PyTorch implementation of Soft Actor-Critic (SAC)

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

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)

PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

Pytorch Implementation of Reinforcement Learning Algorithms ( Soft Actor Critic(SAC)/ DDPG / TD3 /DQN / A2C/ PPO / TRPO)

Python library for Reinforcement Learning.