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Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
| Date | Stars |
|---|---|
| 2026-07-24 | 3217 |
| 2026-07-25 | 3218 |
| 2026-07-28 | 3218 |
| 2026-07-30 | 3218 |
| 2026-08-06 | 3218 |
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# minimalRL-pytorch Implementations of basic RL algorithms with minimal lines of codes! (PyTorch based) * Each algorithm is complete within a single file. * Length of each file is up to 100~150 lines of codes. * Every algorithm can be trained within 30 seconds, even without GPU. * Envs are fixed to "CartPole-v1". You can just focus on the implementations. ## Algorithms 1. [REINFORCE](https://github.com/seungeunrho/minimalRL/blob/master/REINFORCE.py) (67 lines) 2. [Vanilla Actor-Critic](https://github.com/seungeunrho/minimalRL/blob/master/actor_critic.py) (98 lines) 3. [DQN](https://github.com/seungeunrho/minimalRL/blob/master/dqn.py) (112 lines, including replay memory and target network) 4. [PPO](https://github.com/seungeunrho/minimalRL/blob/master/ppo.py) (119 lines, including GAE) 5. [DDPG](https://github.com/seungeunrho/minimalRL/blob/master/ddpg.py) (145 lines, including OU noise and soft target update) 6. [A3C](https://github.com/seungeunrho/minimalRL/blob/master/a3c.py) (129 lines) 7. [ACER](https://github.com/seungeunrho/minimalRL/blob/master/acer.py) (149 lines) 8. [A2C](https://github.com/seungeunrho/minimalRL/blob/master/a2c.py) (188 lines) 9. [SAC](https://github.com/seungeunrho/minimalRL/blob/master/sac.py) (171 lines) added!! 10. [PPO-Continuous](https://github.com/seungeunrho/minimalRL/blob/master/ppo-continuous.py) (161 lines) added!! 11. [Vtrace](https://github.com/seungeunrho/minimalRL/blob/master/vtrace.py) (137 lines) added!! 12. Any suggestion ...? ## Dependencies 1. PyTorch 2. OpenAI GYM ( > 0.26.2 IMPORTANT!! No longer support for the previous versions) ## Usage ```bash # Works only with Python 3. # e.g. python3 REINFORCE.py python3 actor_critic.py python3 dqn.py python3 ppo.py python3 ddpg.py python3 a3c.py python3 a2c.py python3 acer.py python3 sac.py ```
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Rahul Patel · Canada
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1ff1f1788e01993a, topic:reinforcement-learning, topic:deep-reinforcement-learning, readme:actor-critic
matched fp:1ff1f1788e01993a, topic:deep-learning, topic:pytorch