1. Mastering Open-face Chinese Poker by Self-play Reinforcement Learning
Andrew Tan, Andrew Peng, Ajay Shah, Farbod Nowzad

2. Proposal
Use an RL agent to successfully play a stochastic game with no domain knowledge minus the rules
Open-face Chinese Poker (OFCP) is a stochastic, perfect-information zero-sum game played between 2-4 players
Goal: earn more points than your opponent by winning more hands and/or by collecting royalties on premium hands without fouling
Information symmetry
Successes in applying the Alpha Zero technique to Open-face Chinese Poker is a first step in applying the technique to other stochastic games

3. Implementation Problem is modeled as a MDP
Use deep Q-Learning to determine optimal policy at each state
Batch normalization, dropout, ReLU activation
Mean squared error loss, Adam optimizer
Train model against a target network using rollouts between model and random agent
Replace opponent at each iteration with newly trained policy
Tools: Keras, TensorFlow, deuces