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RL-Driven Blackjack Simulator with Action-Masking
Gym-compatible blackjack environment with partial observability and legality-aware action masking. PPO and DQN agents trained via Stable-Baselines3; DQN achieved a 49.4% loss rate, outperforming the house edge.
reinforcement-learningopen-source
Problem
Standard RL blackjack environments ignore partial observability and allow illegal actions, producing agents that don't transfer to realistic play.
Solution
Custom Gym-compatible environment with partial observability (hidden dealer card) and legality-aware action masking. Benchmarked PPO and DQN agents with Stable-Baselines3.
Impact
DQN agent achieved 49.4% loss rate, outperforming the house edge. Open-source benchmark for constrained-action RL environments.
Stack
PythonGymnasiumStable-Baselines3PyTorchNumPy