affaan-m/Behavioral_RL
Reinforcement Learning with human behavioral biases integration
This project helps behavioral scientists and cognitive psychologists simulate human decision-making under risk, specifically within the context of the Iowa Gambling Task. It takes the parameters of the task, including rewards and punishments for different choices, and generates simulated deck selection patterns that closely match observed human behavior. The tool provides a computational model for understanding how people weigh potential gains and losses.
Use this if you are studying human risk assessment and decision-making and want to simulate how people choose between uncertain options, particularly in financial or psychological experiments.
Not ideal if you need a general-purpose reinforcement learning model for engineering problems or a tool for predicting real-world market outcomes.
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Last pushed
Mar 06, 2026
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