Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning

TL;DR

This paper introduces Option Encoder, an auto-encoder framework that compresses multiple options into a concise policy basis, improving efficiency and performance in hierarchical reinforcement learning tasks.

Contribution

It proposes a novel auto-encoder based method with constrained weights to discover a compact policy basis representing multiple options.

Findings

Effective in grid-world environments

Successfully applied to high-dimensional robotic tasks

Reduces redundancy and improves task performance

Abstract

Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent. However, such techniques often yield a large number of options or skills, which can potentially be represented succinctly by filtering out any redundant information. Such a reduction can reduce the required computation while also improving the performance on a target task. In order to compress an array of option policies, we attempt to find a policy basis that accurately captures the set of all options. In this work, we propose Option Encoder, an auto-encoder based framework with intelligently constrained weights, that helps discover a collection of basis policies. The policy basis can be used as a proxy for the original set of skills in a suitable hierarchically organized framework. We demonstrate the efficacy of our method on a collection of grid-worlds and on the high-dimensional Fetch-Reach robotic manipulation task by evaluating the obtained policy basis on a set of downstream tasks.