Smart Walkers in Discrete Space

TL;DR

This paper analyzes the behavior of agents in discrete space, introducing a measure of their learned awareness and demonstrating its effectiveness through analytical, numerical, and chess engine experiments.

Contribution

It proposes a quantitative measure of agents' awareness that correlates with task complexity, validated through analytical and experimental methods.

Findings

Reinforcement learning alters encounter statistics of agents.

Configuration entropy correlates with agents' task performance.

Chess engine tests support the proposed awareness measure.

Abstract

We study the statistical properties of trainable agents moving in discrete space. After introducing the mathematical framework, we first analyze the dynamics of two completely random walkers, mutually competing in a chaser-target interaction scheme. The statistics of the encounters is analytically obtained and the predictions tested versus numerical simulations. We then move forward to extend the baseline case to agents capable of learning and adapting to an external reward signal, using reinforcement learning. Smart walkers morph the statistics of the encounter, to maximize their cumulated reward, as confirmed by combined numerical and analytical insights. More interestingly, configuration entropy proves a reliable proxy to gauge the acquired ability of the agents to cope with the assigned task when no other information about them (i.e. reward signal, policy, etc) is present. We further test the proposed measure of learned skills by operating the Stockfish chess engine against a quasi-random untrained opponent. The obtained conclusions corroborate our claim. Summing up, our primary contribution is to propose and test a quantitative measure of agents' awareness that naturally correlates with the inherent complexity of the task being performed.