Monopoly Using Reinforcement Learning

Arun, E; Rajesh, Harikrishna; Chakrabarti, Debarka; Cherala, Harikiran; George, Koshy

doi:10.1109/tencon.2019.8929523

Cited by 3 publications

(3 citation statements)

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“…This research serves as a catalyst for subsequent studies, inspiring investigations into how deep reinforcement learning techniques might be tailored to handle the complexities of decision-making processes in games like Monopoly, where strategic planning and adaptability are critical components. In [10] "AlphaGo: Mastering the Ancient Game of Go with Machine Learning" by Silver et al ( 2016), the authors present a groundbreaking achievement in artificial intelligence by introducing AlphaGo, a system that mastered the ancient and highly complex game of Go. Utilizing a combination of deep neural networks and reinforcement learning, AlphaGo demonstrated superhuman performance, defeating world champion players.…”

Section: IIImentioning

confidence: 99%

“…RELATED WORK While Monopoly enjoys widespread popularity, previous research on learning-based decision-making for the full game has been limited. Some earlier attempts have modeled Monopoly as a Markov Process [32], while more recent efforts have framed it as an MDP [9] [10]. However, these endeavors focused on simplified versions of the game, restricting actions to buying, selling, or doing nothing, without considering player trades.…”

Section: VImentioning

confidence: 99%

“…For instance, in [9], researchers trained and tested an RL agent against random and fixed-policy opponents using Q-learning and a neural network. Similarly, [10] employed a feed-forward neural network with experience replay to learn gameplay. However, both studies underscored the complexity of maintaining high win rates against diverse opponent strategies.…”

Section: VImentioning

confidence: 99%

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Decision Making in Monopoly Using a Hybrid Deep Reinforcement Learning Approach

Parihar,

2024

IJRASET

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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.

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Section: IIImentioning

confidence: 99%

Section: VImentioning

confidence: 99%

Section: VImentioning

confidence: 99%

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