# Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)

arXiv: https://arxiv.org/abs/1602.03348 · HTML: https://mlsanity.com/paper/1602.03348/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2016.

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

- [Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm](https://mlsanity.com/paper/2102.00168/) (2021)

## Abstract (arXiv)

For complex, high-dimensional Markov Decision Processes (MDPs), it may be necessary to represent the policy with function approximation. A problem is misspecified whenever, the representation cannot express any policy with acceptable performance. We introduce IHOMP : an approach for solving misspecified problems. IHOMP iteratively learns a set of context specialized options and combines these options to solve an otherwise misspecified problem. Our main contribution is proving that IHOMP enjoys theoretical convergence guarantees. In addition, we extend IHOMP to exploit Option Interruption (OI) enabling it to decide where the learned options can be reused. Our experiments demonstrate that IHOMP can find near-optimal solutions to otherwise misspecified problems and that OI can further improve the solutions.