Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
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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 (147 lines, including OU noise and soft target update)
- A3C (129 lines)
- ACER (149 lines)
- A2C added! (188 lines)
- Any suggestion ..?
- OpenAI GYM
# 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