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All Projects → ChenDRAG → mujoco-benchmark

ChenDRAG / mujoco-benchmark

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

Labels

benchmarkperformancepytorchrlbaselineresultsddpgsacdrlmujocoppotianshou

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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:

Example benchmark

SAC

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

Other resources

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