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We present a new approach that merges reinforcement learning with game theory for understanding strategic interactions between neural network agents. Our model leverages decision functions, rooted in game theory, to guide its initial learning process. This is then followed by supervised fine-tuning for deeper comprehension. This approach allows us to analyze the strategic behaviors exhibited by agents within a neural network environment.

R : S × A → ℝ

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CORE UPDATE

Sentiment algorithm update included a number of changes that are relevant to social media sentiment analysis with use of natural language processing (NLP) to understand the sentiment of social media content. NLP is a type of artificial intelligence that can be used to analyze text and identify its emotional tone.
Sentiment analysis update

The core algorithm update is a significant change that will have a major impact on the way that financial news websites are ranked in signal strategy. News that are able to demonstrate topic authority are more likely to rank well.
Topic authority update

The updated machine learning algorithm for core game theory reaction functions is a significant improvement over the previous version. It is more accurate, faster, and includes a number of new features. This makes it a valuable tool for a wide range of applications.
Game theory reaction functions update