Split Q learning: reinforcement learning with two-stream rewards
arXiv preprint arXiv:1906.12350, 2019•arxiv.org
Drawing an inspiration from behavioral studies of human decision making, we propose here
a general parametric framework for a reinforcement learning problem, which extends the
standard Q-learning approach to incorporate a two-stream framework of reward processing
with biases biologically associated with several neurological and psychiatric conditions,
including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder
(ADHD), addiction, and chronic pain. For AI community, the development of agents that react …
a general parametric framework for a reinforcement learning problem, which extends the
standard Q-learning approach to incorporate a two-stream framework of reward processing
with biases biologically associated with several neurological and psychiatric conditions,
including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder
(ADHD), addiction, and chronic pain. For AI community, the development of agents that react …
Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.
arxiv.org
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Bibliography
- Einstein, A., B. Podolsky, and N. Rosen, 1935, “Can quantum-mechanical description of physical reality be considered complete?”, Phys. Rev. 47, 777-780.