Provide full reinforcement learning benchmark on mujoco environments, including ddpg, sac, td3, pg, a2c, ppo, library
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
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:
Supported algorithms are listed below:
Environment | Tianshou | SpinningUp (Pytorch) | SAC paper |
---|---|---|---|
Ant | 5850.2±475.7 | ~3980 | ~3720 |
HalfCheetah | 12138.8±1049.3 | ~11520 | ~10400 |
Hopper | 3542.2±51.5 | ~3150 | ~3370 |
Walker2d | 5007.0±251.5 | ~4250 | ~3740 |
Swimmer | 44.4±0.5 | ~41.7 | N |
Humanoid | 5488.5±81.2 | N | ~5200 |
Reacher | -2.6±0.2 | N | N |
InvertedPendulum | 1000.0±0.0 | N | N |
InvertedDoublePendulum | 9359.5±0.4 | N | N |