Implementation of DQN using just Numpy

Application implements DQN using just Numpy. Thereafter the DQN agent is adapted for the FrozenLake environment, which is also implemented using just Numpy.

Usage

python3 q_learning.py [-mode mode] [-r_mode r_mode] [-model model]

Parameters:

-mode {train, test}            |  application mode
-r_mode {map, weights, stats}  |  vizualization mode
-model MODEL                   |  filename of trained model for testing mode

Examples

python3 q_learning.py -mode train -r_mode map
python3 q_learning.py -mode train -r_mode weights
python3 q_learning.py -mode train -r_mode stats
python3 q_learning.py -mode test -model model.pkl

Training modes

Map mode shows agent's moves in the FrozenLake environment during learning.

Weights mode shows Q-values for every state-action pair during training.

Stats mode prints outcomes of training episodes.

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Created by: Petr Buchal