# Learning to Operate in Open Worlds by Adapting Planning Models

arXiv: https://arxiv.org/abs/2303.14272 · HTML: https://mlsanity.com/paper/2303.14272/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)

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

- [Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World](https://mlsanity.com/paper/2306.12654/) (2023)

## Abstract (arXiv)

Planning agents are ill-equipped to act in novel situations in which their domain model no longer accurately represents the world. We introduce an approach for such agents operating in open worlds that detects the presence of novelties and effectively adapts their domain models and consequent action selection. It uses observations of action execution and measures their divergence from what is expected, according to the environment model, to infer existence of a novelty. Then, it revises the model through a heuristics-guided search over model changes. We report empirical evaluations on the CartPole problem, a standard Reinforcement Learning (RL) benchmark. The results show that our approach can deal with a class of novelties very quickly and in an interpretable fashion.