![logo](./docs/source/figures/logo_1.png)

# XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

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[![PyTorch](https://img.shields.io/badge/PyTorch-%3E%3D1.13.0-red)](https://pytorch.org/get-started/locally/)
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**XuanCe** is an open-source ensemble of Deep Reinforcement Learning (DRL) algorithm implementations.

We call it as **Xuan-Ce (玄策)** in Chinese. 
"**Xuan (玄)**" means incredible and magic box, "**Ce (策)**" means policy.

DRL algorithms are sensitive to hyper-parameters tuning, varying in performance with different tricks,
and suffering from unstable training processes, therefore, sometimes DRL algorithms seems elusive and "Xuan". 
This project gives a thorough, high-quality and easy-to-understand implementation of DRL algorithms, 
and hope this implementation can give a hint on the magics of reinforcement learning.

We expect it to be compatible with multiple deep learning toolboxes(
**[PyTorch](https://pytorch.org/)**, 
**[TensorFlow](https://www.tensorflow.org/)**, and 
**[MindSpore](https://www.mindspore.cn/en)**),
and hope it can really become a zoo full of DRL algorithms.

| **[Full Documentation](https://xuance.readthedocs.io/en/latest/)** |
  **[中文文档](https://xuance.readthedocs.io/zh/latest/)** |
  **[OpenI (启智社区)](https://openi.pcl.ac.cn/OpenRelearnware/XuanCe)** |
  **[XuanCe (Mini version)](https://github.com/wzcai99/xuance)** |

## Currently Included Algorithms

### DRL

<details open>
<summary>(Click to hide)</summary>

- Deep Q Network - DQN [[Paper](https://www.nature.com/articles/nature14236)]
- DQN with Double Q-learning - Double DQN [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/10295)]
- DQN with Dueling network - Dueling DQN [[Paper](http://proceedings.mlr.press/v48/wangf16.pdf)]
- DQN with Prioritized Experience Replay - PER [[Paper](https://arxiv.org/pdf/1511.05952.pdf)]
- DQN with Parameter Space Noise for Exploration - NoisyNet [[Paper](https://arxiv.org/pdf/1706.01905.pdf)]
- Deep Recurrent Q-Netwrk - DRQN [[Paper](https://cdn.aaai.org/ocs/11673/11673-51288-1-PB.pdf)]
- DQN with Quantile Regression - QRDQN [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/11791)]
- Distributional Reinforcement Learning - C51 [[Paper](http://proceedings.mlr.press/v70/bellemare17a/bellemare17a.pdf)]
- Vanilla Policy Gradient - PG [[Paper](https://proceedings.neurips.cc/paper/2001/file/4b86abe48d358ecf194c56c69108433e-Paper.pdf)]
- Phasic Policy Gradient - PPG [[Paper](http://proceedings.mlr.press/v139/cobbe21a/cobbe21a.pdf)] [[Code](https://github.com/openai/phasic-policy-gradient)]
- Advantage Actor Critic - A2C [[Paper](http://proceedings.mlr.press/v48/mniha16.pdf)] [[Code](https://github.com/openai/baselines/tree/master/baselines/a2c)]
- Soft actor-critic based on maximum entropy - SAC [[Paper](http://proceedings.mlr.press/v80/haarnoja18b/haarnoja18b.pdf)] [[Code](http://github.com/haarnoja/sac)]
- Soft actor-critic for discrete actions - SAC-Discrete [[Paper](https://arxiv.org/pdf/1910.07207.pdf)] [[Code](https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch)]
- Proximal Policy Optimization with clipped objective - PPO-Clip [[Paper](https://arxiv.org/pdf/1707.06347.pdf)] [[Code]( https://github.com/berkeleydeeprlcourse/homework/tree/master/hw4)]
- Proximal Policy Optimization with KL divergence - PPO-KL [[Paper](https://arxiv.org/pdf/1707.06347.pdf)] [[Code]( https://github.com/berkeleydeeprlcourse/homework/tree/master/hw4)]
- Deep Deterministic Policy Gradient - DDPG [[Paper](https://arxiv.org/pdf/1509.02971.pdf)] [[Code](https://github.com/openai/baselines/tree/master/baselines/ddpg)]
- Twin Delayed Deep Deterministic Policy Gradient - TD3 [[Paper](http://proceedings.mlr.press/v80/fujimoto18a/fujimoto18a.pdf)][[Code](https://github.com/sfujim/TD3)]
- Parameterised deep Q network - P-DQN [[Paper](https://arxiv.org/pdf/1810.06394.pdf)]
- Multi-pass parameterised deep Q network - MP-DQN [[Paper](https://arxiv.org/pdf/1905.04388.pdf)] [[Code](https://github.com/cycraig/MP-DQN)]
- Split parameterised deep Q network - SP-DQN [[Paper](https://arxiv.org/pdf/1810.06394.pdf)]
</details>

### Multi-Agent Reinforcement Learning (MARL)
<details open>
<summary>(Click to hide)</summary>

- Independent Q-learning - IQL [[Paper](https://hal.science/file/index/docid/720669/filename/Matignon2012independent.pdf)] [[Code](https://github.com/oxwhirl/pymarl)]
- Value Decomposition Networks - VDN [[Paper](https://arxiv.org/pdf/1706.05296.pdf)] [[Code](https://github.com/oxwhirl/pymarl)]
- Q-mixing networks - QMIX [[Paper](http://proceedings.mlr.press/v80/rashid18a/rashid18a.pdf)] [[Code](https://github.com/oxwhirl/pymarl)]
- Weighted Q-mixing networks - WQMIX [[Paper](https://proceedings.neurips.cc/paper/2020/file/73a427badebe0e32caa2e1fc7530b7f3-Paper.pdf)] [[Code](https://github.com/oxwhirl/wqmix)]
- Q-transformation - QTRAN [[Paper](http://proceedings.mlr.press/v97/son19a/son19a.pdf)] [[Code](https://github.com/Sonkyunghwan/QTRAN)]
- Deep Coordination Graphs - DCG [[Paper](http://proceedings.mlr.press/v119/boehmer20a/boehmer20a.pdf)] [[Code](https://github.com/wendelinboehmer/dcg)]
- Independent Deep Deterministic Policy Gradient - IDDPG [[Paper](https://proceedings.neurips.cc/paper/2017/file/68a9750337a418a86fe06c1991a1d64c-Paper.pdf)]
- Multi-agent Deep Deterministic Policy Gradient - MADDPG [[Paper](https://proceedings.neurips.cc/paper/2017/file/68a9750337a418a86fe06c1991a1d64c-Paper.pdf)] [[Code](https://github.com/openai/maddpg)]
- Counterfactual Multi-agent Policy Gradient - COMA [[Paper](https://ojs.aaai.org/index.php/AAAI/article/view/11794)] [[Code](https://github.com/oxwhirl/pymarl)]
- Multi-agent Proximal Policy Optimization - MAPPO [[Paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/9c1535a02f0ce079433344e14d910597-Paper-Datasets_and_Benchmarks.pdf)] [[Code](https://github.com/marlbenchmark/on-policy)]
- Mean-Field Q-learning - MFQ [[Paper](http://proceedings.mlr.press/v80/yang18d/yang18d.pdf)] [[Code](https://github.com/mlii/mfrl)]
- Mean-Field Actor-Critic - MFAC [[Paper](http://proceedings.mlr.press/v80/yang18d/yang18d.pdf)] [[Code](https://github.com/mlii/mfrl)]
- Independent Soft Actor-Critic - ISAC 
- Multi-agent Soft Actor-Critic - MASAC [[Paper](https://arxiv.org/pdf/2104.06655.pdf)]
- Multi-agent Twin Delayed Deep Deterministic Policy Gradient - MATD3 [[Paper](https://arxiv.org/pdf/1910.01465.pdf)]

</details>

## Supported Environments

### [Classic Control](https://www.gymlibrary.dev/environments/classic_control/)

<details open>
<summary>(Click to hide)</summary>

<table rules="none" align="center"><tr>
<td> <center>
<img src="./docs/source/figures/toy/cart_pole.gif" height=100" /><br/><font color="AAAAAA">CartPole</font>
</center></td>
<td> <center>
<img src="./docs/source/figures/toy/pendulum.gif" height=100" /> <br/> <font color="AAAAAA">Pendulum</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/toy/acrobot.gif" height=100" /> <br/> <font color="AAAAAA">Acrobot</font>
</center> </td>
<td> <center>
<br/> <font color="AAAAAA">...</font>
</tr>
</table>

</details>

### [Box2D](https://www.gymlibrary.dev/environments/box2d/)

<details open>
<summary>(Click to hide)</summary>

<table rules="none" align="center"><tr>
<td> <center>
<img src="./docs/source/figures/box2d/bipedal_walker.gif" height=100" /><br/><font color="AAAAAA">CartPole</font>
</center></td>
<td> <center>
<img src="./docs/source/figures/box2d/car_racing.gif" height=100" /> <br/> <font color="AAAAAA">Pendulum</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/box2d/lunar_lander.gif" height=100" /> <br/> <font color="AAAAAA">Acrobot</font>
</center> </td>
</tr>
</table>

</details>

### [MuJoCo Environments](https://www.gymlibrary.dev/environments/mujoco/)

<details open>
<summary>(Click to hide)</summary>

<table rules="none" align="center"><tr>
<td> <center>
<img src="./docs/source/figures/mujoco/ant.gif" height=100" /><br/><font color="AAAAAA">Ant</font>
</center></td>
<td> <center>
<img src="./docs/source/figures/mujoco/half_cheetah.gif" height=100" /> <br/> <font color="AAAAAA">HalfCheetah</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/mujoco/hopper.gif" height=100" /> <br/> <font color="AAAAAA">Hopper</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/mujoco/humanoid.gif" height=100" /> <br/> <font color="AAAAAA">Humanoid</font>
</center> </td>
<td> <center>
<br/> <font color="AAAAAA">...</font>
</center> </td>
</tr>
</table>
</details>

### [Atari Environments](https://www.gymlibrary.dev/environments/atari/)

<details open>
<summary>(Click to hide)</summary>

<table rules="none" align="center"><tr>
<td> <center>
<img src="./docs/source/figures/atari/breakout.gif" height=100" /><br/><font color="AAAAAA">Breakout</font>
</center></td>
<td> <center>
<img src="./docs/source/figures/atari/boxing.gif" height=100" /> <br/> <font color="AAAAAA">Boxing</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/atari/alien.gif" height=100" /> <br/> <font color="AAAAAA">Alien</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/atari/adventure.gif" height=100" /> <br/> <font color="AAAAAA">Adventure</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/atari/air_raid.gif" height=100" /> <br/> <font color="AAAAAA">Air Raid</font>
</center> </td>
<td> <center>
<br/> <font color="AAAAAA">...</font>
</center> </td>
</tr>
</table>

</details>

### [MPE Environments](https://pettingzoo.farama.org/environments/mpe/)

<details open>
<summary>(Click to hide)</summary>

<table rules="none" align="center"><tr>
<td> <center>
<img src="./docs/source/figures/mpe/mpe_simple_push.gif" height=100" /><br/><font color="AAAAAA">Simple Push</font>
</center></td>
<td> <center>
<img src="./docs/source/figures/mpe/mpe_simple_reference.gif" height=100" /> <br/> <font color="AAAAAA">Simple Reference</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/mpe/mpe_simple_spread.gif" height=100" /> <br/> <font color="AAAAAA">Simple Spread</font>
</center> </td>
<td> <center>
<br/> <font color="AAAAAA">...</font>
</center> </td>
</tr>
</table>

</details>

### [Magent2](https://magent2.farama.org/)

<details open>
<summary>(Click to hide)</summary>

<table rules="none" align="center"><tr>
<td> <center>
<img src="./docs/source/figures/magent/battle.gif" height=100" /><br/><font color="AAAAAA">Battle</font>
</center></td>
<td> <center>
<img src="./docs/source/figures/magent/tiger_deer.gif" height=100" /> <br/> <font color="AAAAAA">Tiger Deer</font>
</center> </td>
<td> <center>
<img src="./docs/source/figures/magent/battlefield.gif" height=100" /> <br/> <font color="AAAAAA">Battle Field</font>
</center> </td>
<td> <center>
<br/> <font color="AAAAAA">...</font>
</center> </td>
</tr>
</table>

</details>

### [SMAC](https://github.com/oxwhirl/smac)

StarCraft Multi-Agentt Challenge.

## Installation

The library can be run at Linux, Windows, MacOS, and EulerOS, etc.

Before installing **XuanCe**, you should install [Anaconda](https://www.anaconda.com/download) to prepare a python environment.
(Note: select a proper version of Anaconda from [**here**](https://repo.anaconda.com/archive/).)

After that, open a terminal and install **XuanCe** by the following steps.

**Step 1**: Create a new conda environment (python>=3.7 is suggested):

```commandline
conda create -n xpolicy python=3.7
```

**Step 2**: Activate conda environment:

```commandline
conda activate xpolicy
```

**Step 3**: Install the library:

```commandline
pip install xuance
```

This command does not include the dependencies of deep learning toolboxes. To install the **XuanCe** with 
deep learning tools, you can type `pip install xuance[torch]` for [PyTorch](https://pytorch.org/get-started/locally/),
`pip install xuance[tensorflow]` for [TensorFlow2](https://www.tensorflow.org/install),
`pip install xuance[mindspore]` for [MindSpore](https://www.mindspore.cn/install/en),
and `pip install xuance[all]` for all dependencies.

Note: Some extra packages should be installed manually for further usage. 

## Basic Usage

### Quickly Start

#### Train a Model

```python
import xuance as xp

runner = xp.get_runner(method='dqn', 
                       env='classic_control', 
                       env_id='CartPole-v1', 
                       is_test=False)
runner.run()
```

#### Test the Model

```python
import xuance as xp

runner_test = xp.get_runner(method='dqn', 
                            env='classic_control', 
                            env_id='CartPole-v1', 
                            is_test=True)
runner_test.run()
```

## Logger
You can use tensorboard to visualize what happened in the training process. After training, the log file will be automatically generated in the directory ".results/" and you should be able to see some training data after running the command.
``` 
$ tensorboard --logdir ./logs/dqn/torch/CartPole-v0
```
<!-- If everything going well, you should get a similar display like below. 

![Tensorboard](docs/source/figures/debug.png) -->

## Part of Benchmarks

### Mujoco Environment

| Task           | DDPG   | TD3     | PG     | A2C     | PPO    | PPG     | SAC     |
|----------------|--------|---------|--------|---------|--------|---------|---------|
| Ant-v4         | 1472.8 | 4822.9  | 317.53 | 1420.4  | 2810.7 | 775.26  | 727.25  |
| HalfCheetah-v4 | 10093  | 10718.1 | 891.27 | 2674.5  | 4628.4 | 1235.76 | 6663.20 |
| Hopper-v4      | 3434.9 | 3492.4  | 5380   | 825.9   | 3450.1 | 174.5   | 2436.96 |
| Walker2d-v4    | 2443.7 | 4307.9  | 316.21 | 970.6   | 4318.6 | 46.83   | 1367.31 |
| Swimmer-v4     | 67.7   | 59.9    | 33.54  | 51.4    | 108.9  | 37.69   | 43.82   |
| Humanoid-v4    | 99     | 547.88  | 322.05 | 240.9   | 705.5  | 78.29   | 358.70  |
| Reacher-v4     | -4.05  | -4.07   | -19.20 | -11.7   | -8.1   | -6.76   | -2.67   |
| Ipendulum-v4   | 1000   | 1000    | 1000   | 1000    | 1000   | 160.40  | 1000    |
| IDPendulum-v4  | 9359.8 | 9358.9  | 481.93 | 9357.8  | 9359.1 | 7023.87 | 9359.81 |

### Atari Environment (Ongoing)

| Task                    | DQN      | C51      | PPO     |
|-------------------------|----------|----------|---------|
| ALE/AirRaid-v5          | 7316.67  | 5450.00  | 9283.33 |
| ALE/Alien-v5            | 2676.67  | 2413.33  | 2313.33 |
| ALE/Amidar-v5           | 627.00   | 293.0    | 964.67  |
| ALE/Assault-v5          | 9981.67  | 9088.67  | 6265.67 |
| ALE/Asterix-v5          | 30516.67 | 12866.67 | 2900.00 |
| ALE/Asteroids-v5        | 1393.33  | 2180.0   | 3430.00 |

[//]: # (| ALE/Atlantis-v5         | 294600.0 | 30266.67 |         |)

[//]: # (| ALE/BankHeist-v5        | 1190.0   |          |         |)

[//]: # (| ALE/BattleZone-v5       |          |          |         |)

[//]: # (| ALE/BeamRider-v5        |          |          |         |)

[//]: # (| ALE/Berzerk-v5          |          |          |         |)

[//]: # (| ALE/Bowling-v5          | 92.00    | 56.67    | 76.00   |)

[//]: # (| ALE/Boxing-v5           |          |          |         |)

[//]: # (| ALE/Breakout-v5         | 415.33   | 431.0    | 371.67  |)

[//]: # (| ALE/Carnival-v5         |          |          |         |)

[//]: # (| ALE/Centipede-v5        |          |          |         |)

[//]: # (| ALE/ChopperCommand-v5   |          |          |         |)

[//]: # (| ALE/CrazyClimber-v5     |          |          |         |)

[//]: # (| ALE/Defender-v5         | 11550.0  |          |         |)

[//]: # (| ALE/DemonAttack-v5      |          |          |         |)

[//]: # (| ALE/DoubleDunk-v5       |          |          |         |)

[//]: # (| ALE/ElevatorAction-v5   |          |          |         |)

[//]: # (| ALE/Enduro-v5           |          |          |         |)

[//]: # (| ALE/FishingDerby-v5     |          |          |         |)

[//]: # (| ALE/Freeway-v5          | 34.00    | 33.0     | 34.0    |)

[//]: # (| ALE/Frostbite-v5        |          |          |         |)

[//]: # (| ALE/Gopher-v5           |          |          |         |)

[//]: # (| ALE/Gravitar-v5         |          |          |         |)

[//]: # (| ALE/Hero-v5             |          |          |         |)

[//]: # (| ALE/IceHockey-v5        |          |          |         |)

[//]: # (| ALE/Jamesbond-v5        |          |          |         |)

[//]: # (| ALE/JourneyEscape-v5    |          |          |         |)

[//]: # (| ALE/Kangaroo-v5         |          |          |         |)

[//]: # (| ALE/Krull-v5            |          |          |         |)

[//]: # (| ALE/KungFuMaster-v5     |          |          |         |)

[//]: # (| ALE/MontezumaRevenge-v5 |          |          |         |)

[//]: # (| ALE/MsPacman-v5         | 4650.00  | 4690.00  | 4120.00 |)

[//]: # (| ALE/NameThisGame-v5     |          |          |         |)

[//]: # (| ALE/Phoenix-v5          |          |          |         |)

[//]: # (| ALE/Pitfall-v5          |          |          |         |)

[//]: # (| ALE/Pong-v5             | 21.0     | 20.0     | 21.0    |)

[//]: # (| ALE/Pooyan-v5           |          |          |         |)

[//]: # (| ALE/PrivateEye-v5       |          |          |         |)

[//]: # (| ALE/Qbert-v5            | 16350.0  | 12875.0  | 20050.0 |)

[//]: # (| ALE/Riverraid-v5        |          |          |         |)

[//]: # (| ALE/RoadRunner-v0       |          |          |         |)

[//]: # (| ALE/Robotank-v0         |          |          |         |)

[//]: # (| ALE/Seaquest-v0         |          |          |         |)

[//]: # (| ALE/Skiing-v0           |          |          |         |)

[//]: # (| ALE/Solaris-v5          |          |          |         |)

[//]: # (| ALE/SpaceInvaders-v5    |          |          |         |)

[//]: # (| ALE/StarGunner-v5       |          |          |         |)

[//]: # (| ALE/Tennis-v5           |          |          |         |)

[//]: # (| ALE/TimePilot-v5        |          |          |         |)

[//]: # (| ALE/Tutankham-v5        |          |          |         |)

[//]: # (| ALE/UpNDown-v5          |          |          |         |)

[//]: # (| ALE/Venture-v5          |          |          |         |)

[//]: # (| ALE/VideoPinball-v5     |          |          |         |)

[//]: # (| ALE/WizardOfWor-v5      |          |          |         |)

[//]: # (| ALE/Zaxxon-v5           |          |          |         |)



[//]: # (### MPE Environment &#40;Ongoing&#41;)

### StarCraft2 Environment (Ongoing)

[//]: # (Test win rate &#40;%&#41;:)

[//]: # ()
[//]: # (| Map              | IQL         | VDN         | QMIX        | WQMIX       | DCG         | COMA | VDAC | MAPPO |)

[//]: # (|------------------|-------------|-------------|-------------|-------------|-------------|------|------|-------|)

[//]: # (| 2m_vs_1z         | 100.0 &#40;0.0&#41; | 100.0 &#40;0.0&#41; | 100.0 &#40;0.0&#41; | 100.0 &#40;0.0&#41; | 100.0 &#40;0.0&#41; |      |      |       |)

[//]: # (| 3m               | 100.0 &#40;0.0&#41; | 100.0 &#40;0.0&#41; | 100.0 &#40;0.0&#41; | 100.0 &#40;0.0&#41; | 100.0 &#40;0.0&#41; |      |      |       |)

[//]: # (| 5m               |             |             |             |             |             |      |      |       |)

[//]: # (| 8m               |             |             |             |             |             |      |      |       |)

[//]: # (| 2s3z             |             |             |             |             |             |      |      |       |)

[//]: # (| 3s5z             |             |             |             |             |             |      |      |       |)

[//]: # (| 1c_3s_5z         |             |             |             |             |             |      |      |       |)

[//]: # (| so_many_baneling |             |             |             |             |             |      |      |       |)

[//]: # (| 8m_vs_9m         |             |             |             |             |             |      |      |       |)

[//]: # (| 3s_vs_5z         |             |             |             |             |             |      |      |       |)

[//]: # (| 3s5z             |             |             |             |             |             |      |      |       |)

[//]: # (| MMM2             |             |             |             |             |             |      |      |       |)

[//]: # (| micro_focus      |             |             |             |             |             |      |      |       |)

```
@misc{XuanCe2023,
    title={XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library},
    author={Wenzhang Liu, Wenzhe Cai, Kun Jiang, Guangran Cheng, Yuanda Wang, 
    Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun},
    publisher = {GitHub},
    year={2023},
}
```
