# The syntax and semantics of goals

arXiv: https://arxiv.org/abs/2609.19448 · HTML: https://mlsanity.com/paper/2609.19448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 82 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Distributional Reinforcement Learning with Quantile Regression](https://mlsanity.com/paper/1710.10044/) (2017)
- [Implicit Quantile Networks for Distributional Reinforcement Learning](https://mlsanity.com/paper/1806.06923/) (2018)
- [Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning](https://mlsanity.com/paper/2010.03950/) (2020)
- [A Definition of Continual Reinforcement Learning](https://mlsanity.com/paper/2307.11046/) (2023)
- [The Value Equivalence Principle for Model-Based Reinforcement Learning](https://mlsanity.com/paper/2011.03506/) (2020)
- [On the Expressivity of Markov Reward](https://mlsanity.com/paper/2111.00876/) (2021)
- [Environment-Independent Task Specifications via GLTL](https://mlsanity.com/paper/1704.04341/) (2017)
- [A Boolean Task Algebra for Reinforcement Learning](https://mlsanity.com/paper/2001.01394/) (2020)
- [People construct simplified mental representations to plan](https://mlsanity.com/paper/2105.06948/) (2021)
- [Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States](https://mlsanity.com/paper/2102.05261/) (2021)
- [Unifying task specification in reinforcement learning](https://mlsanity.com/paper/1609.01995/) (2016)
- [Continual Learning as Computationally Constrained Reinforcement Learning](https://mlsanity.com/paper/2307.04345/) (2023)
- [Settling the Reward Hypothesis](https://mlsanity.com/paper/2212.10420/) (2022)
- [Multi-Agent Reinforcement Learning with Temporal Logic Specifications](https://mlsanity.com/paper/2102.00582/) (2021)
- [General agents contain world models](https://mlsanity.com/paper/2506.01622/) (2025)
- [Goals as Reward-Producing Programs](https://mlsanity.com/paper/2405.13242/) (2024)
- [Evolution and The Knightian Blindspot of Machine Learning](https://mlsanity.com/paper/2501.13075/) (2025)
- [Optimizing Return Distributions with Distributional Dynamic Programming](https://mlsanity.com/paper/2501.13028/) (2025)
- [Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs](https://mlsanity.com/paper/2505.12049/) (2025)

## Abstract (arXiv)

In both cognitive science and computer science, goals are conceptualized as cognitive states that flexibly combine with world knowledge to organize and specify purposeful behavior. In this way, goals are compositional representations whose content relates to rational behavior. We here draw attention to goals as representations and their content because it highlights a parallel with other areas in cognitive science - in particular, the syntax-semantics interface in linguistics and logic - while also foregrounding foundational questions about the expressivity, design, and efficiency of different goal representations. For example, goals are typically taken as fixed and imposing constraints on desirable behaviors, but we can also identify constraints on goal representations themselves, such as whether a particular goal language is sufficiently expressive to capture behaviors of interest, or whether different goal representations capture the same behavior. Here, we synthesize work that aims to characterize the properties of different goal representations and suggest these are points of a broader design space. We close by discussing how distinguishing the form and meaning of goals can elucidate the implicit assumptions we make about goals, inform the study of interactions between higher-level cognition and motivation, and isolate axes of variation for different conceptions of goals.