This repo has implementations of reinforcement learning (RL) algorithms in Keras to learn to play Atari Pong. This is what I wrote in the process of learning about these RL algorithms, so it's not well-tuned or performant. If you're looking for a reference implementation to compare against, check out OpenAI baselines.

• pong-ppo.py - PPO. Defeats the "computer" opponent after 300 episodes of training.
• pong-pg.py - Policy gradient (REINFORCE algorithm). Defeats the "computer" opponent after 400 episodes of training.
• pong-actor-critic.py - On-policy batch actor-critic. Defeats the "computer" opponent after 300 episodes of training.
• pong-ddqn-batch.py - Off-policy double Q learning. Defeats the "computer" opponent after 2000 episodes of training.
• pong-ddqn-per.py - Off-policy double Q learning with prioritized experience replay. Defeats the "computer" opponent after 1000 episodes of training.

• Python 3.6
• NumPy
• Tensorflow 1.14
• OpenAI gym