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deep-rl-0.3.7
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| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deep-rl-0.3.7 |
| نام | deep-rl |
| نسخه کتابخانه | 0.3.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jonáš Kulhánek |
| ایمیل نویسنده | jonas.kulhanek@live.com |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/deep-rl/ |
| مجوز | MIT License |
# Deep RL PyTorch
[](https://singularity-hub.org/collections/2581)
This repo contains implementation of popular Deep RL algorithms. Furthermore it contains unified interface for training and evaluation with unified model saving and visualization. It can be used as a good starting point when implementing new RL algorithm in PyTorch.
## Getting started
If you want to base your algorithm on this repository, start by installing it as a package
```
pip install git+https://github.com/jkulhanek/deep-rl-pytorch.git
```
If you want to run attached experiments yourself, feel free to clone this repository.
```
git clone https://github.com/jkulhanek/deep-rl-pytorch.git
```
All dependencies are prepared in a docker container. If you have nvidia-docker enabled, you can use this image. To pull and start the image just run:
```
docker run --runtime=nvidia --net=host -it kulhanek/deep-rl-pytorch:latest bash
```
From there, you can either clone your own repository containing your experiments or clone this one.
## Concepts
All algorithms are implemented as base classes. In your experiment your need to subclass from those base classes. The `deep_rl.core.AbstractTrainer` class is used for all trainers and all algorithms inherit this class. Each trainer can be wrapped in several wrappers (classes extending `deep_rl.core.AbstractWrapper`). Those wrappers are used for saving, logging, terminating the experiment and etc. All experiments should be registered using `@deep_rl.register_trainer` decorator. This decorator than wraps the trainer with default wrappers. This can be controlled by passing arguments to the decorator. All registered trainers (experiments) can be run by calling `deep_rl.make_trainer(<<name>>).run()`.
## Implemented algorithms
### A2C
A2C is a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C) [2] which according to OpenAI [1] gives equal performance. It is however more efficient for GPU utilization.
Start your experiment by subclassing `deep_rl.a2c.A2CTrainer`.
Several models are included in `deep_rl.a2c.model`. You may want to use at least some helper modules contained in this package when designing your own experiment.
In most of the models, initialization is done according to [3].
### Asynchronous Advantage Actor Critic (A3C) [2]
This implementation uses multiprocessing. It comes with two optimizers - RMSprop and Adam.
### Actor Critic using Kronecker-Factored Trust Region (ACKTR) [1]
This is an improvement of A2C described in [1].
## Experiments
> Comming soon
## Requirements
Those packages must be installed before using the framework for your own algorithm:
- OpenAI baselines (can be installed by running `pip install git+https://github.com/openai/baselines.git`)
- PyTorch
- Visdom (`pip install visdom`)
- Gym (`pip install gym`)
- MatPlotLib
Those packages must be installed prior running experiments:
- DeepMind Lab
- Gym[atari]
## Sources
This repository is based on work of several other authors. We would like to express our thanks.
- https://github.com/openai/baselines/tree/master/baselines
- https://github.com/ikostrikov/pytorch-a2c-ppo-acktr/tree/master/a2c_ppo_acktr
- https://github.com/miyosuda/unreal
- https://github.com/openai/gym
## References
[1] Wu, Y., Mansimov, E., Grosse, R.B., Liao, S. and Ba, J., 2017. Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation. In Advances in neural information processing systems (pp. 5279-5288).
[2] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D. and Kavukcuoglu, K., 2016, June. Asynchronous methods for deep reinforcement learning. In International conference on machine learning (pp. 1928-1937).
[3] Saxe, A.M., McClelland, J.L. and Ganguli, S., 2013. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120.
نیازمندی
| مقدار | نام |
|---|---|
| - | matplotlib |
| - | numpy |
| - | torch |
| - | gym |
نحوه نصب
نصب پکیج whl deep-rl-0.3.7:
pip install deep-rl-0.3.7.whl
نصب پکیج tar.gz deep-rl-0.3.7:
pip install deep-rl-0.3.7.tar.gz