# The Quest for a Common Model of the Intelligent Decision Maker

arXiv: https://arxiv.org/abs/2202.13252 · HTML: https://mlsanity.com/paper/2202.13252/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 26 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017)
- [Reward-Respecting Subtasks for Model-Based Reinforcement Learning](https://mlsanity.com/paper/2202.03466/) (2022)

## Built on by (most cited first, 16 of 16)

- [Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability](https://mlsanity.com/paper/2405.17398/) (2024)
- [A Definition of Continual Reinforcement Learning](https://mlsanity.com/paper/2307.11046/) (2023)
- [InfinityDrive: Breaking Time Limits in Driving World Models](https://mlsanity.com/paper/2412.01522/) (2024)
- [The Alberta Plan for AI Research](https://mlsanity.com/paper/2208.11173/) (2022)
- [RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback](https://mlsanity.com/paper/2603.08561/) (2026)
- [Dynamic Datasets and Market Environments for Financial Reinforcement Learning](https://mlsanity.com/paper/2304.13174/) (2023)
- [Three Dogmas of Reinforcement Learning](https://mlsanity.com/paper/2407.10583/) (2024)
- [Cogito, Ergo Ludo: An Agent that Learns to Play by Reasoning and Planning](https://mlsanity.com/paper/2509.25052/) (2025)
- [Harnessing Discrete Representations For Continual Reinforcement Learning](https://mlsanity.com/paper/2312.01203/) (2023)
- [Robust and Communication-Efficient Federated Domain Adaptation via Random Features](https://mlsanity.com/paper/2311.04686/) (2023)
- [A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety](https://mlsanity.com/paper/2407.18422/) (2024)
- [On the Interplay Between Sparsity and Training in Deep Reinforcement Learning](https://mlsanity.com/paper/2501.16729/) (2025)
- [Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning](https://mlsanity.com/paper/2304.01844/) (2023)
- [Agents Explore the Environment Beyond Good Actions to Improve Their Model for Better Decisions](https://mlsanity.com/paper/2306.03408/) (2023)
- [MARVEL: A Multi Agent-based Research Validator and Enabler using Large Language Models](https://mlsanity.com/paper/2601.03436/) (2026)
- [Artifacts as Memory Beyond the Agent Boundary](https://mlsanity.com/paper/2604.08756/) (2026)

## Abstract (arXiv)

The premise of the Multi-disciplinary Conference on Reinforcement Learning and Decision Making is that multiple disciplines share an interest in goal-directed decision making over time. The idea of this paper is to sharpen and deepen this premise by proposing a perspective on the decision maker that is substantive and widely held across psychology, artificial intelligence, economics, control theory, and neuroscience, which I call the "common model of the intelligent agent". The common model does not include anything specific to any organism, world, or application domain. The common model does include aspects of the decision maker's interaction with its world (there must be input and output, and a goal) and internal components of the decision maker (for perception, decision-making, internal evaluation, and a world model). I identify these aspects and components, note that they are given different names in different disciplines but refer essentially to the same ideas, and discuss the challenges and benefits of devising a neutral terminology that can be used across disciplines. It is time to recognize and build on the convergence of multiple diverse disciplines on a substantive common model of the intelligent agent.