| ## If you are curious. |
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| ##### Train a Cartpole agent and watch it play once it converges! |
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| Here's a list of commands to run to quickly get a working example: |
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| <img src="../../data/cartpole.gif" width="25%" /> |
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|
| ```bash |
| # Train model and save the results to cartpole_model.pkl |
| python -m baselines.run --alg=deepq --env=CartPole-v0 --save_path=./cartpole_model.pkl --num_timesteps=1e5 |
| # Load the model saved in cartpole_model.pkl and visualize the learned policy |
| python -m baselines.run --alg=deepq --env=CartPole-v0 --load_path=./cartpole_model.pkl --num_timesteps=0 --play |
| ``` |
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| ## If you wish to apply DQN to solve a problem. |
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| Check out our simple agent trained with one stop shop `deepq.learn` function. |
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| - [baselines/deepq/experiments/train_cartpole.py](experiments/train_cartpole.py) - train a Cartpole agent. |
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| 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. Complimentary file `enjoy_cartpole.py` loads and visualizes the learned policy. |
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| ## If you wish to experiment with the algorithm |
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| ##### Check out the examples |
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| - [baselines/deepq/experiments/custom_cartpole.py](experiments/custom_cartpole.py) - Cartpole training with more fine grained control over the internals of DQN algorithm. |
| - [baselines/deepq/defaults.py](defaults.py) - settings for training on atari. Run |
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| ```bash |
| python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4 |
| ``` |
| to train on Atari Pong (see more in repo-wide [README.md](../../README.md#training-models)) |
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