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https://github.com/lilianweng/deep-reinforcement-learning-gym
Deep reinforcement learning model implementation in Tensorflow + OpenAI gym
https://github.com/lilianweng/deep-reinforcement-learning-gym
deep-learning deep-reinforcement-learning openai-gym tensorflow tensorflow-tutorials
Last synced: about 15 hours ago
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Deep reinforcement learning model implementation in Tensorflow + OpenAI gym
- Host: GitHub
- URL: https://github.com/lilianweng/deep-reinforcement-learning-gym
- Owner: lilianweng
- Created: 2018-04-30T17:41:11.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T23:53:12.000Z (over 1 year ago)
- Last Synced: 2024-09-27T21:02:18.191Z (about 2 months ago)
- Topics: deep-learning, deep-reinforcement-learning, openai-gym, tensorflow, tensorflow-tutorials
- Language: Python
- Homepage: https://lilianweng.github.io/lil-log/2018/05/05/implementing-deep-reinforcement-learning-models.html
- Size: 954 KB
- Stars: 282
- Watchers: 8
- Forks: 92
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Common Deep Reinforcement Learning Models (Tensorflow + OpenAI Gym)
In this repo, I implemented several classic deep reinforcement learning models in Tensorflow and OpenAI gym environment. Please check the corresponding blog post: ["Implementing Deep Reinforcement Learning Models"](http://lilianweng.github.io/lil-log/2018/05/05/implementing-deep-reinforcement-learning-models.html) for more information.
I will add more model implementation in the future.
### Setup
(1) Make sure you have [Homebrew](https://docs.brew.sh/Installation) installed:
```
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
```
(2) Then set up virtualenv.
```
# Install python virtualenv
brew install pyenv-virtualenv
# Create a virtual environment of any name you like with Python 3.6.4 support
pyenv virtualenv 3.6.4 workspace
# Activate the virtualenv named “workspace”
pyenv activate workspace
```
(3) (In the virtual env) Install OpenAI gym according to the [instruction](https://github.com/openai/gym#installation). For a minimal installation, run:
```
git clone https://github.com/openai/gym.git
cd gym
pip install -e .
```
If you are interested in playing with Atari games or other advanced packages in the gym environment, Please go with the gym [instruction](https://github.com/openai/gym#installation) further.
(4) Clone the code repo and install the requirements.
```
git clone [email protected]:lilianweng/deep-reinforcement-learning-gym.git
cd deep-reinforcement-learning-gym
pip install -e . # install the “playground” project.
pip install -r requirements.txt # install required packages.
```
### Train Models
The model configuration can be fully represented in a json file. I have a couple example config files in `playground/configs/data/`.
Start a model training as follows,
```bash
cd playground
python learn.py configs/data/reinforce-cartpole-v1.json
```
During training, three folders will be created in the root directory: `logs`, `checkpoints` and `figs`. Because the env is wrapped by `gym.wrappers.Monitor`, the gym training log is written into `/tmp/` in the meantime. Feel free to comment that out in `playground.configs.manager.ConfigManager` if you are not a fan of that.
Meanwhile, you can start the tensorboard,
```bash
tensorboard --logdir=logs
```
Once the training is complete, two figures are generated in `figs/`.
