wassname/pytorch-soft-actor-critic
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pranz24
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### Description
------------
Reimplementation of [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement
Learning with a Stochastic Actor](https://arxiv.org/pdf/1801.01290.pdf) and [Soft Actor-Critic Algorithms and Applications](https://arxiv.org/pdf/1812.05905.pdf).
Learning with a Stochastic Actor](https://arxiv.org/pdf/1801.01290.pdf).
### Requirements
@@ -13,7 +13,6 @@ Learning with a Stochastic Actor](https://arxiv.org/pdf/1801.01290.pdf) and [Sof
### Run
------------
(Note: There is no need for setting Temperature(`--alpha`) if `--automatic_entropy_tuning` is True.)
#### For SAC :
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python main.py --env-name Humanoid-v2 --aplha 0.05 --tau 1 --target_update_interval 1000
```
#### For SAC (Deterministic, Hard Update):
```
python main.py --env-name Humanoid-v2 --policy Deterministic --tau 1 --target_update_interval 1000
```
### Default Parameters
-------------
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| learning rate(`--lr`) | 3x10<sup>−4</sup> |
| discount(`--gamma`) (γ) | 0.99 |
| replay buffer size(`--replay_size`) | 1x10<sup>6</sup> |
| automatic_entropy_tuning(`--automatic_entropy_tuning`)|False|
|number of hidden layers (all networks)|2|
|number of hidden units per layer(`--hidden_size`)|256|
|number of samples per minibatch(`--batch_size`)|256|