rl_algorithms

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Structural implementation of RL key algorithms

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## Contents

* [Welcome!](https://github.com/medipixel/rl_algorithms#welcome)
* [Contributors](https://github.com/medipixel/rl_algorithms#contributors)
* [Algorithms](https://github.com/medipixel/rl_algorithms#algorithms)
* [Performance](https://github.com/medipixel/rl_algorithms#performance)
* [Getting Started](https://github.com/medipixel/rl_algorithms#getting-started)
* [Class Diagram](https://github.com/medipixel/rl_algorithms#class-diagram)
* [References](https://github.com/medipixel/rl_algorithms#references)

## Welcome!
This repository contains Reinforcement Learning algorithms which are being used for research activities at Medipixel. The source code will be frequently updated. 
We are warmly welcoming external contributors! :)

|<img src="https://user-images.githubusercontent.com/17582508/52840582-18c76e80-313d-11e9-9752-3d6138f39a15.gif" width="260" height="180"/>|<img src="https://media.giphy.com/media/ZxLNajigOcLyeUnOwg/giphy.gif" width="160" height="180"/>|<img src="https://media.giphy.com/media/1mikGEln2lArKMQ6Pt/giphy.gif" width="260" height="180"/>|
|:---:|:---:|:---:|
|BC agent on LunarLanderContinuous-v2|RainbowIQN agent on PongNoFrameskip-v4|SAC agent on Reacher-v2|

## Contributors

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):



<table>
 <tr>
 <td align="center"><a href="https://github.com/Curt-Park"><img src="https://avatars3.githubusercontent.com/u/14961526?v=4?s=100" width="100px;" alt=""/> Jinwoo Park (Curt)</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=Curt-Park" title="Code">💻</a></td>
 <td align="center"><a href="https://github.com/MrSyee"><img src="https://avatars3.githubusercontent.com/u/17582508?v=4?s=100" width="100px;" alt=""/> Kyunghwan Kim</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=MrSyee" title="Code">💻</a></td>
 <td align="center"><a href="https://github.com/darthegg"><img src="https://avatars3.githubusercontent.com/u/16010242?v=4?s=100" width="100px;" alt=""/> darthegg</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=darthegg" title="Code">💻</a></td>
 <td align="center"><a href="https://github.com/mclearning2"><img src="https://avatars3.githubusercontent.com/u/43226417?v=4?s=100" width="100px;" alt=""/> Mincheol Kim</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=mclearning2" title="Code">💻</a></td>
 <td align="center"><a href="https://github.com/minseop4898"><img src="https://avatars1.githubusercontent.com/u/34338299?v=4?s=100" width="100px;" alt=""/> 김민섭</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=minseop4898" title="Code">💻</a></td>
 <td align="center"><a href="https://github.com/jinPrelude"><img src="https://avatars1.githubusercontent.com/u/16518993?v=4?s=100" width="100px;" alt=""/> Leejin Jung</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=jinPrelude" title="Code">💻</a></td>
 <td align="center"><a href="https://github.com/cyoon1729"><img src="https://avatars2.githubusercontent.com/u/33583101?v=4?s=100" width="100px;" alt=""/> Chris Yoon</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=cyoon1729" title="Code">💻</a></td>
 </tr>
 <tr>
 <td align="center"><a href="https://jiseonghan.github.io/"><img src="https://avatars2.githubusercontent.com/u/48741026?v=4?s=100" width="100px;" alt=""/> Jiseong Han</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=jiseongHAN" title="Code">💻</a></td>
 <td align="center"><a href="https://github.com/sehyun-hwang"><img src="https://avatars3.githubusercontent.com/u/23437715?v=4?s=100" width="100px;" alt=""/> Sehyun Hwang</a> <a href="#maintenance-sehyun-hwang" title="Maintenance">🚧</a></td>
 <td align="center"><a href="https://github.com/isk03276"><img src="https://avatars.githubusercontent.com/u/23740495?v=4?s=100" width="100px;" alt=""/> eunjin</a> <a href="https://github.com/medipixel/rl_algorithms/commits?author=isk03276" title="Code">💻</a></td>
 </tr>
</table>

This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification.

## Algorithms

0. [Advantage Actor-Critic (A2C)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/a2c)
1. [Deep Deterministic Policy Gradient (DDPG)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/ddpg)
2. [Proximal Policy Optimization Algorithms (PPO)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/ppo)
3. [Twin Delayed Deep Deterministic Policy Gradient Algorithm (TD3)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/td3)
4. [Soft Actor Critic Algorithm (SAC)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/sac)
5. [Behaviour Cloning (BC with DDPG, SAC)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/bc)
6. [From Demonstrations (DDPGfD, SACfD, DQfD)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/fd)
7. [Rainbow DQN](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/dqn)
8. [Rainbow IQN (without DuelingNet)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/dqn) - DuelingNet [degrades performance](https://github.com/medipixel/rl_algorithms/pull/137)
9. Rainbow IQN (with [ResNet](https://github.com/medipixel/rl_algorithms/blob/master/rl_algorithms/common/networks/backbones/resnet.py))
10. [Recurrent Replay DQN (R2D1)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/recurrent)
11. [Distributed Pioritized Experience Replay (Ape-X)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/common/apex)
12. [Policy Distillation](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/distillation)
13. [Generative Adversarial Imitation Learning (GAIL)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/gail)
14. [Sample Efficient Actor-Critic with Experience Replay (ACER)](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/acer)

## Performance

We have tested each algorithm on some of the following environments.
- [PongNoFrameskip-v4](https://github.com/medipixel/rl_algorithms/tree/master/configs/pong_no_frameskip_v4)
- [LunarLanderContinuous-v2](https://github.com/medipixel/rl_algorithms/tree/master/configs/lunarlander_continuous_v2)
- [LunarLander_v2](https://github.com/medipixel/rl_algorithms/tree/master/configs/lunarlander_v2)
- [Reacher-v2](https://github.com/medipixel/rl_algorithms/tree/master/configs/reacher-v2)

❗Please note that this won't be frequently updated.

#### PongNoFrameskip-v4

**RainbowIQN** learns the game incredibly fast! It accomplishes the perfect score (21) [within 100 episodes](https://app.wandb.ai/curt-park/dqn/runs/b2p9e9f7/logs)!
The idea of RainbowIQN is roughly suggested from [W. Dabney et al.](https://arxiv.org/pdf/1806.06923.pdf).

See [W&B Log](https://app.wandb.ai/curt-park/dqn/reports?view=curt-park%2FPong%20%28DQN%20%2F%20C51%20%2F%20IQN%20%2F%20IQN%20-double%20q%29) for more details. (The performance is measured on the commit [4248057](https://github.com/medipixel/rl_algorithms/pull/158))

![pong_dqn](https://user-images.githubusercontent.com/17582508/56282434-1e93fd00-614a-11e9-9c31-af32e119d5b6.png)

**RainbowIQN with ResNet**'s performance and learning speed were similar to those of RainbowIQN. Also we confirmed that **R2D1 (w/ Dueling, PER)** converges well in the Pong enviornment, though not as fast as RainbowIQN (in terms of update step).

Although we were only able to test **Ape-X DQN (w/ Dueling)** with 4 workers due to limitations to computing power, we observed a significant speed-up in carrying out update steps (with batch size 512). Ape-X DQN learns Pong game in about 2 hours, compared to 4 hours for serial Dueling DQN.

See [W&B Log](https://app.wandb.ai/medipixel_rl/PongNoFrameskip-v4/reports/200626-integration-test--VmlldzoxNTE1NjE) for more details. (The performance is measured on the commit [9e897ad](https://github.com/medipixel/rl_algorithms/commit/9e897adfe93600c1db85ce1a7e064064b025c2c3))
![pong dqn with resnet & rnn](https://user-images.githubusercontent.com/17582508/85813189-80fc7a80-b79d-11ea-96cf-947a62e380f3.png)

![apex dqn](https://user-images.githubusercontent.com/17582508/85814263-83ac9f00-b7a0-11ea-9cdc-ff29de9a6d54.png)

#### LunarLander-v2 / LunarLanderContinuous-v2

We used these environments just for a quick verification of each algorithm, so some of experiments may not show the best performance.

##### 👇 Click the following lines to see the figures.
<details><summary>LunarLander-v2: RainbowDQN, RainbowDQfD, R2D1 </summary>
 
See <a href="https://app.wandb.ai/medipixel_rl/LunarLander-v2/reports/200626-integration-test--VmlldzoxNTE2MzA">W&B log</a> for more details. (The performance is measured on the commit <a href="https://github.com/medipixel/rl_algorithms/commit/9e897adfe93600c1db85ce1a7e064064b025c2c3">9e897ad</a>)

![lunarlander-v2_dqn](https://user-images.githubusercontent.com/17582508/85815561-a5f3ec00-b7a3-11ea-8d7c-8d54953d0c07.png)

</details>

<details><summary>LunarLander-v2:ACER, RainbowDQN, R2D1</summary>
 
See <a herf="https://wandb.ai/chaehyeuk-lee/LunarLander-v2/reports/LunarLander-v2-ACER--VmlldzoxMDU4OTQ1?accessToken=yxrr1h1t2d4n3j22hjz4ktzzgkpuhrm7txlyfpl3jb74les23vbfovvw5g64xgtg">W&B log</a> for more details. (The performance is measured on the commit <a href="https://github.com/medipixel/rl_algorithms/pull/298/commits/82fae77f55f94bb4bc3fb7fc9c44b54dc232c4ff">82fae77</a>)

![lunarlander-v2_acer](https://user-images.githubusercontent.com/48741026/134847201-c7ce6d9f-e930-497f-9473-05da7620095b.png)

</details>

<details><summary>LunarLanderContinuous-v2: A2C, PPO, DDPG, TD3, SAC</summary>
 
See <a href="https://app.wandb.ai/medipixel_rl/LunarLanderContinuous-v2/reports/200626-integration-test--VmlldzoxNDg1MjU">W&B log</a> for more details. (The performance is measured on the commit <a href="https://github.com/medipixel/rl_algorithms/commit/9e897adfe93600c1db85ce1a7e064064b025c2c3">9e897ad</a>)

![lunarlandercontinuous-v2_baselines](https://user-images.githubusercontent.com/17582508/85818298-43065300-b7ab-11ea-9ee0-1eda855498ed.png)

</details>

<details><summary>LunarLanderContinuous-v2: DDPG, DDPGfD, BC-DDPG</summary>
 
See <a href="https://app.wandb.ai/medipixel_rl/LunarLanderContinuous-v2/reports/200626-integration-test--VmlldzoxNDg1MjU">W&B log</a> for more details. (The performance is measured on the commit <a href="https://github.com/medipixel/rl_algorithms/commit/9e897adfe93600c1db85ce1a7e064064b025c2c3">9e897ad</a>)

![lunarlandercontinuous-v2_ddpg](https://user-images.githubusercontent.com/17582508/85818519-c9bb3000-b7ab-11ea-9473-08476a959a0c.png)

</details>

<details><summary>LunarLanderContinuous-v2: SAC, SACfD, BC-SAC</summary>
 
See <a href="https://app.wandb.ai/medipixel_rl/LunarLanderContinuous-v2/reports/200626-integration-test--VmlldzoxNDg1MjU">W&B log</a> for more details. (The performance is measured on the commit <a href="https://github.com/medipixel/rl_algorithms/commit/9e897adfe93600c1db85ce1a7e064064b025c2c3">9e897ad</a>)

![lunarlandercontinuous-v2_sac](https://user-images.githubusercontent.com/17582508/85818654-1acb2400-b7ac-11ea-8641-d559839cab62.png)

</details>

<details><summary>LunarLanderContinuous-v2: PPO, SAC, GAIL</summary>
 
See <a href="https://wandb.ai/chaehyeuk-lee/LunarLanderContinuous-v2?workspace=user-chaehyeuk-lee">W&B log</a> for more details. (The performance is measured on the commit <a href="https://github.com/medipixel/rl_algorithms/commit/922222b2e249f1f14bdf1a28c9f0f00752e49907">9e897ad</a>)

![lunarlandercontinuous-v2_gail](https://user-images.githubusercontent.com/23740495/130401442-8b668975-8760-4a79-b757-1c1e9a9c4e47.png)

</details>

#### Reacher-v2

We reproduced the performance of **DDPG**, **TD3**, and **SAC** on Reacher-v2 (Mujoco). They reach the score around -3.5 to -4.5.

##### 👇 Click the following the line to see the figures.

<details><summary>Reacher-v2: DDPG, TD3, SAC</summary>

See [W&B Log](https://app.wandb.ai/medipixel_rl/reacher-v2/reports?view=curt-park%2FBaselines%20%23158) for more details.

![reacher-v2_baselines](https://user-images.githubusercontent.com/17582508/56282421-163bc200-614a-11e9-8d4d-2bb520575fbb.png)

</details>

## Getting started

#### Prerequisites
* This repository is tested on [Anaconda](https://www.anaconda.com/distribution/) virtual environment with python 3.6.1+
    ```
    $ conda create -n rl_algorithms python=3.7.9
    $ conda activate rl_algorithms
    ```
* In order to run Mujoco environments (e.g. `Reacher-v2`), you need to acquire [Mujoco license](https://www.roboti.us/license.html).

#### Installation
First, clone the repository.
```
git clone https://github.com/medipixel/rl_algorithms.git
cd rl_algorithms
```

###### For users
Install packages required to execute the code. It includes `python setup.py install`. Just type:
```
make dep
```

###### For developers
If you want to modify code you should configure formatting and linting settings. It automatically runs formatting and linting when you commit the code. Contrary to `make dep` command, it includes `python setup.py develop`. Just type:

```
make dev
```

After having done `make dev`, you can validate the code by the following commands.
```
make format  # for formatting
make test  # for linting
```

#### Usages
You can train or test `algorithm` on `env_name` if `configs/env_name/algorithm.yaml` exists. (`configs/env_name/algorithm.yaml` contains hyper-parameters)
```
python run_env_name.py --cfg-path <config-path>
```

e.g. running soft actor-critic on LunarLanderContinuous-v2.
```
python run_lunarlander_continuous_v2.py --cfg-path ./configs/lunarlander_continuous_v2/sac.yaml <other-options>
```

e.g. running a custom agent, **if you have written your own configs**: `configs/env_name/ddpg-custom.yaml`.
```
python run_env_name.py --cfg-path ./configs/lunarlander_continuous_v2/ddpg-custom.py
```
You will see the agent run with hyper parameter and model settings you configured.

#### Arguments for run-files

In addition, there are various argument settings for running algorithms. If you check the options to run file you should command 
```
python <run-file> -h
```
- `--test`
 - Start test mode (no training).
- `--off-render`
 - Turn off rendering.
- `--log`
 - Turn on logging using [W&B](https://www.wandb.com/).
- `--seed <int>`
 - Set random seed.
- `--save-period <int>`
 - Set saving period of model and optimizer parameters.
- `--max-episode-steps <int>`
 - Set maximum episode step number of the environment. If the number is less than or equal to 0, it uses the default maximum step number of the environment.
- `--episode-num <int>`
 - Set the number of episodes for training.
- `--render-after <int>`
 - Start rendering after the number of episodes.
- `--load-from <save-file-path>`
 - Load the saved models and optimizers at the beginning.

#### Show feature map with Grad-CAM and Saliency-map
You can show a feature map that the trained agent extract using **[Grad-CAM(Gradient-weighted Class Activation Mapping)](https://arxiv.org/pdf/1610.02391.pdf)** and **[Saliency map](https://arxiv.org/pdf/1312.6034.pdf)**.

Grad-CAM is a way of combining feature maps using the gradient signal, and produce a coarse localization map of the important regions in the image. You can use it by adding [Grad-CAM config](https://github.com/medipixel/rl_algorithms/blob/master/configs/pong_no_frameskip_v4/dqn.py#L39) and `--grad-cam` flag when you run. For example:
```
python run_env_name.py --cfg-path <config-path> --test --grad-cam
```
The results will be rendered as follows:

You can also use Saliency-map in a similar way to Grad-CAM just by adding `--saliency-map` flag. Saliency-map need trained weight carried by `--load-from` flag. 
```
python run_env_name.py --cfg-path <config-path> --load-from <save-file-path> --test --saliency-map
```
Saliency map will be stored in data/saliency_map

Both Grad-CAM and Saliency-map can be only used for the agent that uses convolutional layers like **DQN for Pong environment**. You can see feature maps of all the configured convolution layers.

#### Using policy distillation

We seperate the document about using policy distillation in [rl_algorithms/distillation/README.md](https://github.com/medipixel/rl_algorithms/tree/master/rl_algorithms/distillation).

#### W&B for logging
We use [W&B](https://www.wandb.com/) for logging of network parameters and others. For logging, please follow the steps below after requirement installation:

>0. Create a [wandb](https://www.wandb.com/) account
>1. Check your **API key** in settings, and login wandb on your terminal: `$ wandb login API_KEY`
>2. Initialize wandb: `$ wandb init`

For more details, read [W&B tutorial](https://docs.wandb.com/docs/started.html).

## Class Diagram
Class diagram at [#135](https://github.com/medipixel/rl_algorithms/pull/135).

❗This won't be frequently updated.

![RL_Algorithms_ClassDiagram](https://user-images.githubusercontent.com/16010242/55934443-812d5a80-5c6b-11e9-9b31-fa8214965a55.png)

## Citing the Project
To cite this repository in publications:
```
@misc{rl_algorithms,
  author = {Kim, Kyunghwan and Lee, Chaehyuk and Jeong, Euijin and Han, Jiseong and Kim, Minseop and Yoon, Chris and Kim, Mincheol and Park, Jinwoo},
  title = {Medipixel RL algorithms},
  year = {2020},
  publisher = {Github},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/medipixel/rl_algorithms}},
}
```
## References
0. [T. P. Lillicrap et al., "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971, 2015.](https://arxiv.org/pdf/1509.02971.pdf)
1. [J. Schulman et al., "Proximal Policy Optimization Algorithms." arXiv preprint arXiv:1707.06347, 2017.](https://arxiv.org/abs/1707.06347.pdf)
2. [S. Fujimoto et al., "Addressing function approximation error in actor-critic methods." arXiv preprint arXiv:1802.09477, 2018.](https://arxiv.org/pdf/1802.09477.pdf)
3. [T.  Haarnoja et al., "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor." arXiv preprint arXiv:1801.01290, 2018.](https://arxiv.org/pdf/1801.01290.pdf)
4. [T. Haarnoja et al., "Soft Actor-Critic Algorithms and Applications." arXiv preprint arXiv:1812.05905, 2018.](https://arxiv.org/pdf/1812.05905.pdf)
5. [T. Schaul et al., "Prioritized Experience Replay." arXiv preprint arXiv:1511.05952, 2015.](https://arxiv.org/pdf/1511.05952.pdf)
6. [M. Andrychowicz et al., "Hindsight Experience Replay." arXiv preprint arXiv:1707.01495, 2017.](https://arxiv.org/pdf/1707.01495.pdf)
7. [A. Nair et al., "Overcoming Exploration in Reinforcement Learning with Demonstrations." arXiv preprint arXiv:1709.10089, 2017.](https://arxiv.org/pdf/1709.10089.pdf)
8. [M. Vecerik et al., "Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards."arXiv preprint arXiv:1707.08817, 2017](https://arxiv.org/pdf/1707.08817.pdf)
9. [V. Mnih et al., "Human-level control through deep reinforcement learning." Nature, 518
(7540):529–533, 2015.](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)
10. [van Hasselt et al., "Deep Reinforcement Learning with Double Q-learning." arXiv preprint arXiv:1509.06461, 2015.](https://arxiv.org/pdf/1509.06461.pdf)
11. [Z. Wang et al., "Dueling Network Architectures for Deep Reinforcement Learning." arXiv preprint arXiv:1511.06581, 2015.](https://arxiv.org/pdf/1511.06581.pdf)
12. [T. Hester et al., "Deep Q-learning from Demonstrations." arXiv preprint arXiv:1704.03732, 2017.](https://arxiv.org/pdf/1704.03732.pdf)
13. [M. G. Bellemare et al., "A Distributional Perspective on Reinforcement Learning." arXiv preprint arXiv:1707.06887, 2017.](https://arxiv.org/pdf/1707.06887.pdf)
14. [M. Fortunato et al., "Noisy Networks for Exploration." arXiv preprint arXiv:1706.10295, 2017.](https://arxiv.org/pdf/1706.10295.pdf)
15. [M. Hessel et al., "Rainbow: Combining Improvements in Deep Reinforcement Learning." arXiv preprint arXiv:1710.02298, 2017.](https://arxiv.org/pdf/1710.02298.pdf)
16. [W. Dabney et al., "Implicit Quantile Networks for Distributional Reinforcement Learning." arXiv preprint arXiv:1806.06923, 2018.](https://arxiv.org/pdf/1806.06923.pdf)
17. [Ramprasaath R. Selvaraju et al., "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization." arXiv preprint arXiv:1610.02391, 2016.](https://arxiv.org/pdf/1610.02391.pdf)
18. [Kaiming He et al., "Deep Residual Learning for Image Recognition." arXiv preprint arXiv:1512.03385, 2015.](https://arxiv.org/pdf/1512.03385)
19. [Steven Kapturowski et al., "Recurrent Experience Replay in Distributed Reinforcement Learning." in International Conference on Learning Representations https://openreview.net/forum?id=r1lyTjAqYX, 2019.](https://openreview.net/forum?id=r1lyTjAqYX)
20. [Horgan et al., "Distributed Prioritized Experience Replay." in International Conference on Learning Representations, 2018](https://arxiv.org/pdf/1803.00933.pdf)
21. [Simonyan et al., "Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps", 2013](https://arxiv.org/pdf/1312.6034.pdf)
22. [Ho et al., "Generative adversarial imitation learning", 2016](https://arxiv.org/abs/1606.03476)
23. [Wang, Ziyu, et al. "Sample efficient actor-critic with experience replay", 2016.](https://arxiv.org/pdf/1611.01224.pdf)

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