# Learning Independently-Obtainable Reward Functions

arXiv: https://arxiv.org/abs/1901.08649 · HTML: https://mlsanity.com/paper/1901.08649/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

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

- [Value Function Decomposition for Iterative Design of Reinforcement Learning Agents](https://mlsanity.com/paper/2206.13901/) (2022)

## Abstract (arXiv)

We present a novel method for learning a set of disentangled reward functions that sum to the original environment reward and are constrained to be independently obtainable. We define independent obtainability in terms of value functions with respect to obtaining one learned reward while pursuing another learned reward. Empirically, we illustrate that our method can learn meaningful reward decompositions in a variety of domains and that these decompositions exhibit some form of generalization performance when the environment's reward is modified. Theoretically, we derive results about the effect of maximizing our method's objective on the resulting reward functions and their corresponding optimal policies.