https://github.com/araffin/pytorch_agents

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO) and Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR). Python2 compatible (branch python2)

https://github.com/araffin/pytorch_agents

Keywords

Repository

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO) and Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR). Python2 compatible (branch python2)

https://github.com/araffin/pytorch_agents/blob/master/

# pytorch-a2c-ppo-acktr

## Update 10/06/2017: added enjoy.py and a link to pretrained models!
## Update 09/27/2017: now supports both Atari and MuJoCo/Roboschool!

This is a PyTorch implementation of
* Advantage Actor Critic (A2C), a synchronous deterministic version of [A3C](https://arxiv.org/pdf/1602.01783v1.pdf)
* Proximal Policy Optimization [PPO](https://arxiv.org/pdf/1707.06347.pdf)
* Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation [ACKTR](https://arxiv.org/abs/1708.05144)

Also see the OpenAI posts: [A2C/ACKTR](https://blog.openai.com/baselines-acktr-a2c/) and [PPO](https://blog.openai.com/openai-baselines-ppo/) for more information.

This implementation is inspired by the OpenAI baselines for [A2C](https://github.com/openai/baselines/tree/master/baselines/a2c), [ACKTR](https://github.com/openai/baselines/tree/master/baselines/acktr) and [PPO](https://github.com/openai/baselines/tree/master/baselines/ppo1). It uses the same hyper parameters and the model since they were well tuned for Atari games.

Please use this bibtex if you want to cite this repository in your publications:

    @misc{pytorchrl,
      author = {Kostrikov, Ilya},
      title = {PyTorch Implementations of Reinforcement Learning Algorithms},
      year = {2018},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/ikostrikov/pytorch-a2c-ppo-acktr}},
    }

## Supported (and tested) environments (via [OpenAI Gym](https://gym.openai.com))
* [Atari Learning Environment](https://github.com/mgbellemare/Arcade-Learning-Environment)
* [MuJoCo](http://mujoco.org)
* [PyBullet](http://pybullet.org) (including Racecar, Minitaur and Kuka)

I highly recommend PyBullet as a free open source alternative to MuJoCo for continuous control tasks.

All environments are operated using exactly the same Gym interface. See their documentations for a comprehensive list.

## Requirements

* Python 3 (it might work with Python 2, but I didn't test it)
* [PyTorch](http://pytorch.org/)
* [Visdom](https://github.com/facebookresearch/visdom)
* [OpenAI baselines](https://github.com/openai/baselines)

In order to install requirements, follow:

```bash
# PyTorch
conda install pytorch torchvision -c soumith

# Baselines for Atari preprocessing
git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .

# Other requirements
pip install -r requirements.txt
```

## Contributions

Contributions are very welcome. If you know how to make this code better, please open an issue. If you want to submit a pull request, please open an issue first. Also see a todo list below.

Also I'm searching for volunteers to run all experiments on Atari and MuJoCo (with multiple random seeds).

## Disclaimer

It's extremely difficult to reproduce results for Reinforcement Learning methods. See ["Deep Reinforcement Learning that Matters"](https://arxiv.org/abs/1709.06560) for more information. I tried to reproduce OpenAI results as closely as possible. However, majors differences in performance can be caused even by minor differences in TensorFlow and PyTorch libraries.

### TODO
* Improve this README file. Rearrange images.
* Improve performance of KFAC, see kfac.py for more information
* Run evaluation for all games and algorithms

## Training

Start a `Visdom` server with `python -m visdom.server`, it will serve `http://localhost:8097/` by default.

### Atari
#### A2C

```bash
python main.py --env-name "PongNoFrameskip-v4"
```

#### PPO

```bash
python main.py --env-name "PongNoFrameskip-v4" --algo ppo --use-gae --lr 2.5e-4 --clip-param 0.1 --num-processes 8 --num-steps 128 --num-mini-batch 4 --vis-interval 1 --log-interval 1
```

#### ACKTR

```bash
python main.py --env-name "PongNoFrameskip-v4" --algo acktr --num-processes 32 --num-steps 20
```

### MuJoCo
#### A2C

```bash
python main.py --env-name "Reacher-v1" --num-stack 1 --num-frames 1000000
```

#### PPO

```bash
python main.py --env-name "Reacher-v1" --algo ppo --use-gae --vis-interval 1  --log-interval 1 --num-stack 1 --num-steps 2048 --num-processes 1 --lr 3e-4 --entropy-coef 0 --ppo-epoch 10 --num-mini-batch 32 --gamma 0.99 --tau 0.95 --num-frames 1000000
```

#### ACKTR

ACKTR requires some modifications to be made specifically for MuJoCo. But at the moment, I want to keep this code as unified as possible. Thus, I'm going for better ways to integrate it into the codebase.

## Enjoy

Load a pretrained model from [my Google Drive](https://drive.google.com/open?id=0Bw49qC_cgohKS3k2OWpyMWdzYkk).

Also pretrained models for other games are available on request. Send me an email or create an issue, and I will upload it.

Disclaimer: I might have used different hyper-parameters to train these models.

### Atari

```bash
python enjoy.py --load-dir trained_models/a2c --env-name "PongNoFrameskip-v4" --num-stack 4
```

### MuJoCo

```bash
python enjoy.py --load-dir trained_models/ppo --env-name "Reacher-v1" --num-stack 1
```

## Results

### A2C

![BreakoutNoFrameskip-v4](imgs/a2c_breakout.png)

![SeaquestNoFrameskip-v4](imgs/a2c_seaquest.png)

![QbertNoFrameskip-v4](imgs/a2c_qbert.png)

![beamriderNoFrameskip-v4](imgs/a2c_beamrider.png)

### PPO


![BreakoutNoFrameskip-v4](imgs/ppo_halfcheetah.png)

![SeaquestNoFrameskip-v4](imgs/ppo_hopper.png)

![QbertNoFrameskip-v4](imgs/ppo_reacher.png)

![beamriderNoFrameskip-v4](imgs/ppo_walker.png)


### ACKTR

![BreakoutNoFrameskip-v4](imgs/acktr_breakout.png)

![SeaquestNoFrameskip-v4](imgs/acktr_seaquest.png)

![QbertNoFrameskip-v4](imgs/acktr_qbert.png)

![beamriderNoFrameskip-v4](imgs/acktr_beamrider.png)

Owner

GitHub Events