# The Eigenoption-Critic Framework

arXiv: https://arxiv.org/abs/1712.04065 · HTML: https://mlsanity.com/paper/1712.04065/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2017.

## Built on by (most cited first, 3 of 3)

- [Temporal Abstraction in Reinforcement Learning with the Successor Representation](https://mlsanity.com/paper/2110.05740/) (2021)
- [Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition](https://mlsanity.com/paper/2305.00654/) (2023)
- [Multi-agent Deep Covering Skill Discovery](https://mlsanity.com/paper/2210.03269/) (2022)

## Abstract (arXiv)

Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient exploration. Despite its initial promising results, a couple of issues in current algorithms limit its application, namely: (1) EO methods require two separate steps (eigenoption discovery and reward maximization) to learn a control policy, which can incur a significant amount of storage and computation; (2) EOs are only defined for problems with discrete state-spaces and; (3) it is not easy to take the environment's reward function into consideration when discovering EOs. To addresses these issues, we introduce an algorithm termed eigenoption-critic (EOC) based on the Option-critic (OC) framework [Bacon17], a general hierarchical reinforcement learning (RL) algorithm that allows learning the intra-option policies simultaneously with the policy over options. We also propose a generalization of EOC to problems with continuous state-spaces through the Nystr"om approximation. EOC can also be seen as extending OC to nonstationary settings, where the discovered options are not tailored for a single task.