Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning. However, autonomously learning effective sets of options is still a major challenge in the field. In this paper we focus on the recently introduced idea of using representation learning methods to guide the option discovery process. Specifically, we look at eigenoptions, options obtained from representations that encode diffusive information flow in the environment. We extend the existing algorithms for eigenoption discovery to settings with stochastic transitions and in which handcrafted features are not available.   We propose an algorithm that discovers eigenoptions while learning non-linear state representations from raw pixels. It exploits recent successes in the deep reinforcement learning literature and the equivalence between proto-value functions and the successor representation. We use traditional tabular domains to provide intuition about our approach and Atari 2600 games to demonstrate its potential.
We show how we can use the successor representation to discover eigenoptions in stochastic domains, from raw pixels. Eigenoptions are options learned to navigate the latent dimensions of a learned representation. Extends the idea of eigenoptions to domains with stochastic transitions and where state features are learned. Shows equivalence between proto value functions and successor representations and derives the idea of eigen options as a mechanism in option discovery The paper is a follow up on previous work by Machado et al. (2017) showing how proto-value functions can be used to define options called “eigenoptions”.