Exploiting Abstract Symmetries in Reinforcement Learning for Complex Environments

Reinforcement Learning is rapidly establishing itself as the foremost choice for optimization of sequential autonomous decision-making problems. Encumbered by its sample inefficiency, the extension of the field to large state space and dynamic environments remains an open problem. We present a novel concept that exploits abstract spatial symmetry in complex environments for extending the skills of naively trained agents in local abstractions of the environment. The concept of EASE (Exploitation of Abstract Symmetry of Environments), when incorporated, improves the sample efficiency of traditional reinforcement learning algorithms. The presented work exemplifies the concept of EASE by presenting three distinct settings; EASE with heuristics-based planning, EASE with learning from demonstrations and EASE with state-space abstraction and proposes a novel algorithm for each setting.

Automated summary

This paper presents a novel concept called EASE (Exploitation of Abstract Symmetry of Environments) that aims to improve the sample efficiency of traditional reinforcement learning algorithms by exploiting abstract spatial symmetry in complex environments. The concept is exemplified through three different settings and a novel algorithm is proposed for each setting.