An elegant PyTorch deep reinforcement learning library.
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Updated
May 23, 2024 - Python
An elegant PyTorch deep reinforcement learning library.
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).
Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
OpenDILab Decision AI Engine
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
Python library for Reinforcement Learning.
Real-time behaviour synthesis with MuJoCo, using Predictive Control
Trust Region Policy Optimization with TensorFlow and OpenAI Gym
PyTorch implementation of Soft Actor-Critic (SAC)
Reinforcement learning algorithms for MuJoCo tasks
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
PyTorch implementation of Trust Region Policy Optimization
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
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.
A unified framework for robot learning
DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)
A collection of robotics simulation environments for reinforcement learning
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