This repo only servers as a link to Tianshou's benchmark of Mujoco environments. Latest benchmark is maintained under thu-ml/tianshou. See full benchmark here.

Keywords: deep reinforcement learning, pytorch, mujoco, benchmark, performances, Tianshou, baseline

Tianshou's Mujoco Benchmark

We benchmarked Tianshou algorithm implementations in 9 out of 13 environments from the MuJoCo Gym task suite.

For each supported algorithm and supported mujoco environments, we provide:

• Default hyperparameters used for benchmark and scripts to reproduce the benchmark;
• A comparison of performance (or code level details) with other open source implementations or classic papers;
• Graphs and raw data that can be used for research purposes;
• Log details obtained during training;
• Pretrained agents;
• Some hints on how to tune the algorithm.

Supported algorithms are listed below:

• Deep Deterministic Policy Gradient (DDPG), commit id
• Twin Delayed DDPG (TD3), commit id
• Soft Actor-Critic (SAC), commit id
• REINFORCE algorithm, commit id
• Natural Policy Gradient (NPG), commit id
• Advantage Actor-Critic (A2C), commit id
• Proximal Policy Optimization (PPO), commit id
• Trust Region Policy Optimization (TRPO), commit id
• Trust Region Policy Optimization (ACKTR), commit id

Example benchmark

SAC

Other resources

• Spinningup Benchmark
• OpenAI Baseliens Benchmark
• TODO and relative discussions: 1, 2