Training a vision-based agent with the Deep Q Learning Network (DQN) in Atari's Breakout environment, implementation in Tensorflow.
MIT License
$ pip3 install opencv-python gym "gym[atari]"
BreakoutNoFrameskip-v4
.
├── ./
| ├── agent_dqn.py ─────────── DQN model
| ├── atari_wrapper.py ─────── Atari wrapper
| ├── environment.py ───────── Gym wrapper
| ├── runner.py ────────────── Main program for training and testing
| └── Readme.md ────────────── This file
└── model/
├── dqn_learning_curve_compare.png ──────── Figure 1
├── dqn_best_setting.png ────────────────── Figure 2
├── dqn_learning_curve.png ──────────────── Figure 3
├── checkpoint ──────────────────────────── Tensorflow model check point
├── model_dqn-25581.data-00000-of-00001 ─── Tensorflow model data
├── model_dqn-25581.meta ────────────────── Tensorflow model meta
└── model_dqn-25581.index ───────────────── Tensorflow model index
$ python3 runner.py --train_dqn
$ python3 runner.py --test_dqn
$ python3 runner.py --test_dqn --do_render