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Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
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seungeunrho/minimalRL
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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.
- REINFORCE (67 lines)
- Vanilla Actor-Critic (98 lines)
- DQN (112 lines, including replay memory and target network)
- PPO (119 lines, including GAE)
- DDPG (145 lines, including OU noise and soft target update)
- A3C (129 lines)
- ACER (149 lines)
- A2C (188 lines)
- SAC (171 lines) added!!
- PPO-Continuous (161 lines) added!!
- Vtrace (137 lines) added!!
- Any suggestion ...?
- PyTorch
- OpenAI GYM ( > 0.26.2 IMPORTANT!! No longer support for the previous versions)
# Works only with Python 3.# e.g.python3 REINFORCE.pypython3 actor_critic.pypython3 dqn.pypython3 ppo.pypython3 ddpg.pypython3 a3c.pypython3 a2c.pypython3 acer.pypython3 sac.py
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Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)