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Shangtong Zhang
2018-05-07 22:03:11 -06:00
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@@ -23,6 +23,29 @@ Asynchronous algorithms below are removed in current version but can be found in
Support for PyTorch v0.3.x can be found in [v0.2](https://github.com/ShangtongZhang/DeepRL/releases/tag/v0.2). Note all the figures are generated via this version. After the upgrade to PyTorch v0.4.0, I have only tested the classical control tasks.
# Dependency
* MacOS 10.12 or Ubuntu 16.04
* PyTorch v0.4.0
* Python 3.6, 3.5 or 2.7 (deprecated)
* Core dependencies: `pip install -e .`
* Optional: [Roboschool](https://github.com/openai/roboschool), [DeepMind Control Suite](https://github.com/deepmind/dm_control)+[DMControl2Gym](dm_control2gym)
# Usage
```examples.py``` contains examples for all the implemented algorithms
Please use this bibtex if you want to cite this repo
```
@misc{deeprl,
author = {Shangtong, Zhang},
title = {Modularized Implementation of Deep RL Algorithms in PyTorch},
year = {2018},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/ShangtongZhang/DeepRL}},
}
```
# Curves
> Curves for CartPole are trivial so I didn't place it here, and there isn't any fixed random seed. The curves are generated in the same manner as OpenAI baselines (one run and smoothed by recent 100 episodes)
## DQN
@@ -49,6 +72,7 @@ Support for PyTorch v0.3.x can be found in [v0.2](https://github.com/ShangtongZh
## OC
![Loading...](https://raw.githubusercontent.com/ShangtongZhang/DeepRL/master/images/option_critic_pixel_atari-180417-092617.png)
This is my synchronous option-critic implementation, not the original one.
## Action Conditional Video Prediction
@@ -58,29 +82,6 @@ This is my synchronous option-critic implementation, not the original one.
Prediction is sampled after 110K iterations, and I only implemented one-step training
# Dependency
* MacOS 10.12 or Ubuntu 16.04
* PyTorch v0.4.0
* Python 3.6, 3.5 or 2.7 (deprecated)
* Core dependencies: `pip install -e .`
* Optional: [Roboschool](https://github.com/openai/roboschool), [DeepMind Control Suite](https://github.com/deepmind/dm_control)+[DMControl2Gym](dm_control2gym)
# Usage
```examples.py``` contains examples for all the implemented algorithms
Please use this bibtex if you want to cite this repo
```
@misc{deeprl,
author = {Shangtong, Zhang},
title = {Modularized Implementation of Deep RL Algorithms in PyTorch},
year = {2018},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/ShangtongZhang/DeepRL}},
}
```
# References
* [Human Level Control through Deep Reinforcement Learning](https://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)
* [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783)