/NAF-tensorflow

"Continuous Deep Q-Learning with Model-based Acceleration" in TensorFlow

Primary LanguagePythonMIT LicenseMIT

Normalized Advantage Functions (NAF) in TensorFlow

TensorFlow implementation of Continuous Deep q-Learning with Model-based Acceleration.

algorithm

Requirements

Usage

First, install prerequisites with:

$ pip install tqdm gym[all]

To train a model for an environment with a continuous action space:

$ python main.py --env_name=Pendulum-v0 --is_train=True
$ python main.py --env_name=Pendulum-v0 --is_train=True --display=True

To test and record the screens with gym:

$ python main.py --env_name=Pendulum-v0 --is_train=False
$ python main.py --env_name=Pendulum-v0 --is_train=False --display=True

Results

Training details of Pendulum-v0 with different hyperparameters.

$ python main.py --env_name=Pendulum-v0 # dark green
$ python main.py --env_name=Pendulum-v0 --action_fn=tanh # light green
$ python main.py --env_name=Pendulum-v0 --use_batch_norm=True # yellow
$ python main.py --env_name=Pendulum-v0 --use_seperate_networks=True # green

Pendulum-v0_2016-07-15

References

Author

Taehoon Kim / @carpedm20