# Split Q Learning: Reinforcement Learning with Two-Stream Rewards

arXiv: https://arxiv.org/abs/1906.12350 · HTML: https://mlsanity.com/paper/1906.12350/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [Neural Topic Modeling of Psychotherapy Sessions](https://mlsanity.com/paper/2204.10189/) (2022)
- [Towards Healthy AI: Large Language Models Need Therapists Too](https://mlsanity.com/paper/2304.00416/) (2023)
- [Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook](https://mlsanity.com/paper/2210.13623/) (2022)
- [Working Alliance Transformer for Psychotherapy Dialogue Classification](https://mlsanity.com/paper/2210.15603/) (2022)
- [SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning](https://mlsanity.com/paper/2208.13077/) (2022)
- [Computational Inference in Cognitive Science: Operational, Societal and Ethical Considerations](https://mlsanity.com/paper/2210.13526/) (2022)
- [TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts](https://mlsanity.com/paper/2302.10845/) (2023)
- [A Reinforcement Learning Framework for Online Speaker Diarization](https://mlsanity.com/paper/2302.10924/) (2023)

## Abstract (arXiv)

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.