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A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry
- Source :
- Scopus-Elsevier, Baihan Lin
- Publication Year :
- 2019
-
Abstract
- Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and SARSA methods, as well as recently proposed Double Q-Learning approaches, on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities.<br />Published in AAMAS 2020 as a full paper. This article supersedes our work arXiv:1706.02897 into RL setting and extends extensively into RL games, cognitive modeling, and gambling tasks in lifelong learning setting
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Artificial Intelligence (cs.AI)
Computer Science - Artificial Intelligence
Statistics - Machine Learning
FOS: Biological sciences
Quantitative Biology - Neurons and Cognition
Neurons and Cognition (q-bio.NC)
Machine Learning (stat.ML)
Computer Science - Multiagent Systems
Machine Learning (cs.LG)
Multiagent Systems (cs.MA)
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Scopus-Elsevier, Baihan Lin
- Accession number :
- edsair.doi.dedup.....6cfad3457c1544c6d97a1f1dc49ff01b