History

ChenDRAG 9b61bc620c add logger (#295 )

This PR focus on refactor of logging method to solve bug of nan reward and log interval. After these two pr, hopefully fundamental change of tianshou/data is finished. We then can concentrate on building benchmarks of tianshou finally.

Things changed:

1. trainer now accepts logger (BasicLogger or LazyLogger) instead of writer;
2. remove utils.SummaryWriter;

2021-02-24 14:48:42 +08:00

results/sac

sac mujoco result (#246 )

2020-11-09 16:43:55 +08:00

runnable

add logger (#295 )

2021-02-24 14:48:42 +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?

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