Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach
This addresses the problem of skewed action distributions in board game AI for researchers, but it is incremental as it builds on existing deep RL methods.
The paper tackled decision-making in Monopoly by proposing a hybrid deep reinforcement learning approach that combines deep RL for frequent complex decisions with fixed policies for infrequent straightforward ones, resulting in a 30% improvement in games won against fixed-policy agents.
Learning to adapt and make real-time informed decisions in a dynamic and complex environment is a challenging problem. Monopoly is a popular strategic board game that requires players to make multiple decisions during the game. Decision-making in Monopoly involves many real-world elements such as strategizing, luck, and modeling of opponent's policies. In this paper, we present novel representations for the state and action space for the full version of Monopoly and define an improved reward function. Using these, we show that our deep reinforcement learning agent can learn winning strategies for Monopoly against different fixed-policy agents. In Monopoly, players can take multiple actions even if it is not their turn to roll the dice. Some of these actions occur more frequently than others, resulting in a skewed distribution that adversely affects the performance of the learning agent. To tackle the non-uniform distribution of actions, we propose a hybrid approach that 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 deep reinforcement learning agent by 30% in the number of games won against fixed-policy agents.