A NEW ARCHITECTURE FOR GAME THEORETIC DEEP REINFORCEMENT LEARNING

 

ABSTRACT

Game theory can be applied to the stock market in several ways. One way is to analyze the strategic decision-making of market participants, such as traders, investors, and companies. For example, game theory can be used to understand how traders might make decisions about when to buy or sell a particular stock, or how companies might make decisions about when to issue new stock or buy back existing stock. Another way that game theory can be applied to the stock market is to analyze the overall market dynamics and how they might affect stock prices. For example, game theory can be used to understand how the actions of individual market participants might influence the overall market and how different market conditions, such as supply and demand, might affect stock prices. Game theory and neural networks can be used together in a variety of ways. One way to use game theory with neural networks is to apply game-theoretic concepts and techniques to the design and analysis of neural networks. We trained this model using Reinforcement Learning from decision functions (game theory). We trained an initial model using supervised fine-tuning to understand the strategic behavior of agents that are trained to interact with each other using neural networks. To create a reward model for reinforcement learning, we needed to collect test data, which consisted of two or more model responses statistically ranked by quality. To collect this data, we use best-response functions (represent the action that a player will take in response to the actions of the other players.) Results show that our method can significantly improve the accuracy of game theory based stock prediction models. The accuracy of our method is 92.54%, which is significantly higher than the accuracy of the baseline methods.

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