This is a demo for hyperparameter-tuning with Tune[1] using an DQN example from Udacity[2] for the OpenAI-Gym environment[3] LunarLander[4].
This demo is meant to be able to be trained on cpu locally (took ~40 min. on a 2.5 GHz Quad-Core i7)
Land on the moon and get reward for landing properly, loose reward for using fuel or land outside landing pad. In this example, we use the metric mean_reward for this.
make install(tested with python 3.8)
- watch random, untrained agent via
make random - [optional] configure hyperparameter in
train.py make trainstarts training- see results in tensorboard via
make tensorboard - after training finished,
make gifcreates a .gif of the best model
[1] https://docs.ray.io/en/latest/tune.html
[2] https://github.com/udacity/deep-reinforcement-learning/tree/master/dqn/solution