Where does value come from? In our new preprint (https://psyarxiv.com/rxf7e/ ) we explore some of the open challenges of Reinforcement Learning and reward-based decision-making more broadly. THREAD 1/13
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Our framework – goal equilibrium theory - builds on existing drive reduction theories as well as recent advances linking physiological homeostasis to RL. However, we argue that this framework can be more widely applied to abstract cognitive goals. 7/13
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The key intuition is that the agent represents an abstracted, multi-dimensional value map on which it defines its current position and goals. Thus, the agent will seek to minimise its distance to goal through interactions with the environment. 8/13pic.twitter.com/7Vd9tH5rEn
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Importantly, this circumvents the need to define arbitrary reward functions and simply assumes that the agent keeps track of physiological and cognitive repositories of their assets (e.g. hydration, social status, capital, etc.) 9/13
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Interestingly, simply assuming that more than one value dimension contributes to the agent’s distance-to-goal estimation (eq. 2) yields diminishing marginal utility and convex indifference curves over value dimensions. 10/13pic.twitter.com/pknxUvAHlO
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Although our theoretical framework is preliminary, we think that goal equilibrium theory may scaffold research into complex choices involving multiple, competing goals and inform our understanding of the underlying neural mechanisms. 11/13
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This will require tasks which make explicit reference to multiple assets as if the agent collected distinct items whose relationships to value fluctuate as the agent’s goals change. 12/13
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We hope you enjoy reading the paper and would be happy to hear thoughts, comments, or suggestions. 13/13
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