Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.24963/ijcai.2019/913 | IJCAI |
DocType | Volume | Citations |
Conference | abs/1906.12350 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baihan Lin | 1 | 0 | 3.04 |
| Djallel Bouneffouf | 2 | 4 | 8.88 |
| Guillermo A. Cecchi | 3 | 199 | 34.56 |