Decision Making in Monopoly Using a Hybrid Deep Reinforcement Learning Approach

Abstract

Abstract: This research introduces a novel approach to decision-making in the classic board game Monopoly by leveraging a hybrid deep reinforcement learning framework. Traditional methods for decision-making in Monopoly often rely on heuristic strategies or rule-based systems, which may lack adaptability and fail to capture the dynamic and complex nature of the game. In contrast, this study proposes a hybrid model that combines the strengths of deep learning and reinforcement learning to enable an agent to autonomously learn effective decision-making strategies through interactions with the Monopoly environment. The hybrid deep reinforcement learning model is designed to process and interpret the intricate game state representations, learning optimal decision policies over time. The incorporation of deep neural networks allows the model to capture complex patterns and dependencies in the data, while reinforcement learning enables the agent to iteratively improve its decision-making abilities through trial and error. The research focuses on developing a suitable reward structure and feature representation that enhances the model's ability to navigate the diverse scenarios encountered in Monopoly, ultimately aiming to achieve superior decision-making performance compared to traditional approaches.