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Tianshou/examples/mujoco
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ChenDRAG 3108b9db0d
Add Timelimit trick to optimize policies (#296) 
* consider timelimit.truncated in calculating returns by default
* remove ignore_done
2021-02-26 13:23:18 +08:00
..
results/sac
sac mujoco result (#246)
2020-11-09 16:43:55 +08:00
runnable
Add Timelimit trick to optimize policies (#296)
2021-02-26 13:23:18 +08:00
mujoco_sac.py
add logger (#295)
2021-02-24 14:48:42 +08:00
README.md
sac mujoco result (#246)
2020-11-09 16:43:55 +08:00

README.md

Mujoco Result

SAC (single run)

The best reward computes from 100 episodes returns in the test phase.

SAC on Swimmer-v3 always stops at 47~48.

task 3M best reward parameters time cost (3M)
HalfCheetah-v3 10157.70 ± 171.70 python3 mujoco_sac.py --task HalfCheetah-v3 2~3h
Walker2d-v3 5143.04 ± 15.57 python3 mujoco_sac.py --task Walker2d-v3 2~3h
Hopper-v3 3604.19 ± 169.55 python3 mujoco_sac.py --task Hopper-v3 2~3h
Humanoid-v3 6579.20 ± 1470.57 python3 mujoco_sac.py --task Humanoid-v3 --alpha 0.05 2~3h
Ant-v3 6281.65 ± 686.28 python3 mujoco_sac.py --task Ant-v3 2~3h

Which parts are important?

  1. DO NOT share the same network with two critic networks.
  2. The sigma (of the Gaussian policy) MUST be conditioned on input.
  3. The network size should not be less than 256.
  4. The deterministic evaluation helps a lot :)