wassname/pytorch-soft-actor-critic
1
0
Fork 0
mirror of https://github.com/wassname/pytorch-soft-actor-critic.git synced 2026-08-20 12:40:11 +08:00
Code Issues Packages Projects Releases Wiki Activity

Edit README.md & main.py

Browse Source
This commit is contained in:
pranz24
2019-09-16 16:42:30 +05:30
parent a1fe838d64
commit 5663db7e22
1 changed files with 14 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+14 -12
View File
@@ -20,21 +20,30 @@ Learning with a Stochastic Actor](https://arxiv.org/pdf/1801.01290.pdf) -> [SAC_
### Usage
```
usage: main.py [-h] [--env-name ENV_NAME] [--policy POLICY] [--eval EVAL]
[--gamma G] [--tau G] [--lr G] [--alpha G]
[--automatic_entropy_tuning G] [--seed N] [--batch_size N]
[--num_steps N] [--hidden_size N] [--updates_per_step N]
[--start_steps N] [--target_update_interval N]
[--replay_size N] [--cuda]
```
(Note: There is no need for setting Temperature(`--alpha`) if `--automatic_entropy_tuning` is True.)
#### For SAC :
##### For SAC :
```
python main.py --env-name Humanoid-v2 --aplha 0.05
```
#### For SAC (Hard Update):
##### For SAC (Hard Update):
```
python main.py --env-name Humanoid-v2 --alpha 0.05 --tau 1 --target_update_interval 1000
```
#### For SAC (Deterministic, Hard Update):
##### For SAC (Deterministic, Hard Update):
```
python main.py --env-name Humanoid-v2 --policy Deterministic --tau 1 --target_update_interval 1000
@@ -43,13 +52,6 @@ python main.py --env-name Humanoid-v2 --policy Deterministic --tau 1 --target_up
### Arguments
```
usage: main.py [-h] [--env-name ENV_NAME] [--policy POLICY] [--eval EVAL]
[--gamma G] [--tau G] [--lr G] [--alpha G]
[--automatic_entropy_tuning G] [--seed N] [--batch_size N]
[--num_steps N] [--hidden_size N] [--updates_per_step N]
[--start_steps N] [--target_update_interval N]
[--replay_size N] [--cuda]
PyTorch Soft Actor-Critic Args
optional arguments:
@@ -60,7 +62,7 @@ optional arguments:
--eval EVAL Evaluates a policy a policy every 10 episode (default:
True)
--gamma G discount factor for reward (default: 0.99)
--tau G target smoothing coefficient(τ) (default: 0.005)
--tau G target smoothing coefficient(τ) (default: 5e-3)
--lr G learning rate (default: 3e-4)
--alpha G Temperature parameter α determines the relative
importance of the entropy term against the reward
@@ -72,7 +74,7 @@ optional arguments:
--num_steps N maximum number of steps (default: 1e6)
--hidden_size N hidden size (default: 256)
--updates_per_step N model updates per simulator step (default: 1)
--start_steps N Steps sampling random actions (default: 10<sup>4</sup>)
--start_steps N Steps sampling random actions (default: 1e4)
--target_update_interval N
Value target update per no. of updates per step
(default: 1)