Variations on the Reinforcement Learning performance of Blackjack
This work provides incremental insights into reinforcement learning performance for Blackjack players and researchers, focusing on environment-specific variations.
The study tackled the problem of how deck size affects reinforcement learning convergence in Blackjack, finding that variations in deck size impact the learning rate of a q-learning agent and the effectiveness of card counting strategies.
Blackjack or "21" is a popular card-based game of chance and skill. The objective of the game is to win by obtaining a hand total higher than the dealer's without exceeding 21. The ideal blackjack strategy will maximize financial return in the long run while avoiding gambler's ruin. The stochastic environment and inherent reward structure of blackjack presents an appealing problem to better understand reinforcement learning agents in the presence of environment variations. Here we consider a q-learning solution for optimal play and investigate the rate of learning convergence of the algorithm as a function of deck size. A blackjack simulator allowing for universal blackjack rules is also implemented to demonstrate the extent to which a card counter perfectly using the basic strategy and hi-lo system can bring the house to bankruptcy and how environment variations impact this outcome. The novelty of our work is to place this conceptual understanding of the impact of deck size in the context of learning agent convergence.