Here's a list of commands to run to quickly get a working example:
# Train model and save the results to cartpole_model.pkl
python -m baselines.deepq.experiments.train_cartpole
# Load the model saved in cartpole_model.pkl and visualize the learned policy
python -m baselines.deepq.experiments.enjoy_cartpoleBe sure to check out the source code of both files!
Check out our simple agent trained with one stop shop deepq.learn function.
- 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.
- 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.
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:
python -m baselines.deepq.experiments.atari.download_modelOnce 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.
python -m baselines.deepq.experiments.atari.download_model --blob model-atari-duel-pong-1 --model-dir /tmp/models
python -m baselines.deepq.experiments.atari.enjoy --model-dir /tmp/models/model-atari-duel-pong-1 --env Pong --dueling