This is a reinforcement learning algorithm library. The code takes into account both performance and simplicity, with little dependence. The algorithm code comes from spinningup and some researchers engaged in reinforcement learning.
The project covers the following algorithms：
- DQN, Dueling DQN, D3QN
- DDPG, DDPG-HER, DDPG-PER
- PPO+GAE, Multi-Processing PPO, Discrete PPO
- TD3, Multi-Processing TD3
- SAC
- MADDPG
All the algorithms adopt the pytorch framework. All the codes are combined in the easiest way to understand, which is suitable for beginners of reinforcement learning, but the code performance is excellent.
This project also provides the reference of these algorithms:
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
- CONTINUOUS CONTROL WITH DEEP REINFORCEMENT
- High-Dimensional Continuous Control Using Generalized Advantage Estimation
- Proximal Policy Optimization
- Soft Actor-Critic Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
- Auto alpha Soft Actor-Critic Algorithms and Applications
- Addressing Function Approximation Error in Actor-Critic Methods
Testing environment: 'Pendulum-v0'. What you just need to do is running the main.py. Here are the results of several cases:
spinningup-DDPG reward curve:
spinningup-TD3 reward curve:
spinningup-SAC reward curve:
DRL_algorithm_library
├─ Arm_env.py
├─ DDPG
│ ├─ DDPG
│ ├─ DDPG_spinningup
│ └─ DDPG_spinningup_PER
├─ DQN_family
│ ├─ Agent.py
│ ├─ __pycache__
│ ├─ core.py
│ ├─ main.py
│ └─ runs
├─ MADDPG
│ ├─ .gitignore
│ ├─ .idea
│ ├─ __pycache__
│ ├─ arguments.py
│ ├─ enjoy_split.py
│ ├─ logs
│ ├─ main_openai.py
│ ├─ model.py
│ ├─ models
│ └─ replay_buffer.py
├─ PPO
│ ├─ .idea
│ ├─ DiscretePPO
│ ├─ PPOModel.py
│ ├─ TrainedModel
│ ├─ __pycache__
│ ├─ core.py
│ ├─ draw.py
│ ├─ multi_processing_ppo
│ └─ myPPO.py
├─ README.md
├─ SAC
│ ├─ SAC_demo1
│ └─ SAC_spinningup
├─ TD3
│ ├─ .idea
│ ├─ Multi-Processing-TD3
│ ├─ TD3
│ ├─ TD3_spinningup
│ └─ TrainedModel
├─ __pycache__
│ ├─ Arm_env.cpython-36.pyc
│ └─ Arm_env.cpython-38.pyc
├─ imgs
│ ├─ spinSAC.png
│ ├─ spin_ddpg.png
│ └─ spin_td3.png
└─ reference
├─ 多智能体 MADDPG - Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments - 1706.02275.pdf
├─ 强化学习 DDPG - CONTINUOUS CONTROL WITH DEEP REINFORCEMENT 1509.02971.pdf
├─ 强化学习 GAE High-Dimensional Continuous Control Using Generalized Advantage Estimation 1506.02438.pdf
├─ 强化学习 PPO - Proximal Policy Optimization1707.06347.pdf
├─ 强化学习 SAC1 - Soft Actor-Critic Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor - 1801.01290.pdf
├─ 强化学习 SAC2 auto alpha Soft Actor-Critic Algorithms and Applications 1812.05905.pdf
└─ 强化学习 TD3 - Addressing Function Approximation Error in Actor-Critic Methods 1802.09477.pdf
gym==0.10.5
matplotlib==3.2.2
pytorch==1.7.1
numpy==1.19.2
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