We're releasing OpenAI Baselines, a set of high-quality implementations of reinforcement learning algorithms. To start, we're making available an open source version of Deep Q-Learning and three of its variants.
These algorithms will make it easier for the research community to replicate, refine, and identify new ideas, and will create good baselines to build research on top of. Our DQN implementation and its variants are roughly on par with the scores in published papers. We expect they will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones.
You can install it by typing:
```bash
pip install baselines
```
## If you are curious.
##### Train a Cartpole agent and watch it play once it converges!
Here's a list of commands to run to quickly get a working example:
<imgsrc="data/cartpole.gif"width="25%"/>
```bash
# Train model and save the results to cartpole_model.pkl
-`baselines/deepq/experiments/train_cartpole.py` - train a Cartpole agent.
-`baselines/deepq/experiments/train_pong.py` - train a Pong agent using convolutional neural networks.
In particular notice that once `deepq.learn` finishes training it returns `act` function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. For both of the files listed above there are complimentary files `enjoy_cartpole.py` and `enjoy_pong.py` respectively, that load and visualize the learned policy.
## If you wish to experiment with the algorithm
##### Check out the examples
-`baselines/deepq/experiments/custom_cartpole.py` - Cartpole training with more fine grained control over the internals of DQN algorithm.
-`baselines/deepq/experiments/atari/train.py` - more robust setup for training at scale.
##### Download a pretrained Atari agent
For some research projects it is sometimes useful to have an already trained agent handy. There's a variety of models to choose from. You can list them all by running:
Once you pick a model, you can download it and visualize the learned policy. Be sure to pass `--dueling` flag to visualization script when using dueling models.
