Simple implementations of fundamental reinforcement learning algorithms to learn to play Pong. Instead of using Pixels, the agents perceive the environment using simple hand-crafted features.
Limitations:
- The implementations can only learn to play Pong, but could be adapted to other similar games.
- No optimization has been done - both algorithms could be massively parallelized.
To watch a pre-trained agent in action, just type:
python pgm_evaluate.py
To start the training from scratch, just type:
python pgm_train.py
Result after 5000 batches of 10 episodes each: the agent beats the computer-controlled player most of the times.
To watch a pre-trained agent in action, just type:
python dqn_evaluate.py
To start the training from scratch, just type:
papermill --log-output dqn_train.ipynb dqn_results.ipynb
Result after 10000 episodes: the agent wins most games with nearly perfect score.
