Minimalist & Decoupled Reinforcement Learning.
Project description
Intro
Minimalist&DecoupledReinforcement Learning
Usage
pip install minimalist-RL
import gymnasium as gym
import torch.nn as nn
from minimalist_RL.SAC import SAC, ActorCritic
from minimalist_RL.utils import train_RL
env = gym.make("HalfCheetah-v5")
ac_net = ActorCritic(env, sizes=[256, 256], Act=nn.ReLU)
sac = SAC(ac_net)
train_RL(env, ac_net.pi.tanh_act, sac.update)
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
minimalist_rl-0.0.4.tar.gz
(4.4 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file minimalist_rl-0.0.4.tar.gz.
File metadata
- Download URL: minimalist_rl-0.0.4.tar.gz
- Upload date:
- Size: 4.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
1722b2290520d6c624e861af1c944efd0ac6f4b227e03df2fb41b0357e5541db
|
|
| MD5 |
d922d7d4743974940956bac29b1c7a89
|
|
| BLAKE2b-256 |
229470dfe550ea9c0f8714b79cafaf459ff9d0780bca6e295ff6e2fbc74071b5
|
File details
Details for the file minimalist_rl-0.0.4-py3-none-any.whl.
File metadata
- Download URL: minimalist_rl-0.0.4-py3-none-any.whl
- Upload date:
- Size: 4.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3bb27983ce952d287b654712c7dc166b6150af568126bfa212aaa32f5aeafbd0
|
|
| MD5 |
760eab8c18b3ed8d7d715ac045f942f9
|
|
| BLAKE2b-256 |
7c8e13a7329637a463fa44045165933c2fbfad1158c9dbb607220563ba109507
|