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* SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo
49 lines
1012 B
Markdown
49 lines
1012 B
Markdown
# Soft Actor Critic
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Each experiment uses 3 seeds and is trained for 3M environment steps.
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The parameters used for SAC are the same parameters as described in the [original paper](https://arxiv.org/abs/1801.01290).
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### Inverted Pendulum SAC - single worker
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```bash
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coach -p Mujoco_SAC -lvl inverted_pendulum
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```
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<img src="inverted_pendulum_sac.png" alt="Inverted Pendulum SAC" width="800"/>
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### Hopper Clipped SAC - single worker
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```bash
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coach -p Mujoco_SAC -lvl hopper
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```
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<img src="hopper_sac.png" alt="Hopper SAC" width="800"/>
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### Half Cheetah Clipped SAC - single worker
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```bash
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coach -p Mujoco_SAC -lvl half_cheetah
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```
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<img src="half_cheetah_sac.png" alt="Half Cheetah SAC" width="800"/>
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### Walker 2D Clipped SAC - single worker
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```bash
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coach -p Mujoco_SAC -lvl walker2d
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```
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<img src="walker2d_sac.png" alt="Walker 2D SAC" width="800"/>
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### Humanoid Clipped SAC - single worker
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```bash
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coach -p Mujoco_SAC -lvl humanoid
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```
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<img src="humanoid_sac.png" alt="Humanoid SAC" width="800"/>
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