28 lines
1.2 KiB
Markdown
28 lines
1.2 KiB
Markdown
# Mujoco Result
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## SAC (single run)
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The best reward computes from 100 episodes returns in the test phase.
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SAC on Swimmer-v3 always stops at 47\~48.
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| task | 3M best reward | parameters | time cost (3M) |
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| -------------- | ----------------- | ------------------------------------------------------- | -------------- |
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| HalfCheetah-v3 | 10157.70 ± 171.70 | `python3 mujoco_sac.py --task HalfCheetah-v3` | 2~3h |
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| Walker2d-v3 | 5143.04 ± 15.57 | `python3 mujoco_sac.py --task Walker2d-v3` | 2~3h |
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| Hopper-v3 | 3604.19 ± 169.55 | `python3 mujoco_sac.py --task Hopper-v3` | 2~3h |
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| Humanoid-v3 | 6579.20 ± 1470.57 | `python3 mujoco_sac.py --task Humanoid-v3 --alpha 0.05` | 2~3h |
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| Ant-v3 | 6281.65 ± 686.28 | `python3 mujoco_sac.py --task Ant-v3` | 2~3h |
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### Which parts are important?
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0. DO NOT share the same network with two critic networks.
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1. The sigma (of the Gaussian policy) MUST be conditioned on input.
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2. The network size should not be less than 256.
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3. The deterministic evaluation helps a lot :)
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