# Where do goals come from? A Generic Approach to Autonomous Goal-System Development

arXiv: https://arxiv.org/abs/1410.5557 · HTML: https://mlsanity.com/paper/1410.5557/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2014.

## Built on by (most cited first, 1 of 1)

- [CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives](https://mlsanity.com/paper/2411.19787/) (2024)

## Abstract (arXiv)

Goals express agents' intentions and allow them to organize their behavior based on low-dimensional abstractions of high-dimensional world states. How can agents develop such goals autonomously? This paper proposes a detailed conceptual and computational account to this longstanding problem. We argue to consider goals as high-level abstractions of lower-level intention mechanisms such as rewards and values, and point out that goals need to be considered alongside with a detection of the own actions' effects. We propose Latent Goal Analysis as a computational learning formulation thereof, and show constructively that any reward or value function can by explained by goals and such self-detection as latent mechanisms. We first show that learned goals provide a highly effective dimensionality reduction in a practical reinforcement learning problem. Then, we investigate a developmental scenario in which entirely task-unspecific rewards induced by visual saliency lead to self and goal representations that constitute goal-directed reaching.