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Learning to adapt and make real-time informed decisions in a dynamic andcomplex environment is a challenging problem. Monopoly is a popular strategicboard game that requires players to make multiple decisions during the game.Decision-making in Monopoly involves many real-world elements such asstrategizing, luck, and modeling of opponent's policies. In this paper, wepresent novel representations for the state and action space for the fullversion of Monopoly and define an improved reward function. Using these, weshow that our deep reinforcement learning agent can learn winning strategiesfor Monopoly against different fixed-policy agents. In Monopoly, players cantake multiple actions even if it is not their turn to roll the dice. Some ofthese actions occur more frequently than others, resulting in a skeweddistribution that adversely affects the performance of the learning agent. Totackle the non-uniform distribution of actions, we propose a hybrid approachthat combines deep reinforcement learning (for frequent but complex decisions)with a fixed policy approach (for infrequent but straightforward decisions).Experimental results show that our hybrid agent outperforms a standard deepreinforcement learning agent by 30% in the number of games won againstfixed-policy agents.

Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach