A Dynamics Perspective of Pursuit-Evasion Games of Intelligent Agents with the Ability to Learn

TL;DR

This paper explores pursuit-evasion games involving intelligent agents that learn from experience, using dynamics-based formulations and reinforcement learning to analyze how strategies evolve and affect outcomes in pursuit-evasion scenarios.

Contribution

It introduces a bio-inspired dynamics formulation combined with reinforcement learning to model and analyze pursuit-evasion strategies of intelligent agents.

Findings

Reinforcement learning enhances pursuit and evasion strategies based on experience.

In pursuit-evasion games with dynamics, learned strategies can prevent evaders from escaping faster pursuers.

Results align with natural observations and the principle of survival of the fittest.

Abstract

Pursuit-evasion games are ubiquitous in nature and in an artificial world. In nature, pursuer(s) and evader(s) are intelligent agents that can learn from experience, and dynamics (i.e., Newtonian or Lagrangian) is vital for the pursuer and the evader in some scenarios. To this end, this paper addresses the pursuit-evasion game of intelligent agents from the perspective of dynamics. A bio-inspired dynamics formulation of a pursuit-evasion game and baseline pursuit and evasion strategies are introduced at first. Then, reinforcement learning techniques are used to mimic the ability of intelligent agents to learn from experience. Based on the dynamics formulation and reinforcement learning techniques, the effects of improving both pursuit and evasion strategies based on experience on pursuit-evasion games are investigated at two levels 1) individual runs and 2) ranges of the parameters of pursuit-evasion games. Results of the investigation are consistent with nature observations and the natural law - survival of the fittest. More importantly, with respect to the result of a pursuit-evasion game of agents with baseline strategies, this study achieves a different result. It is shown that, in a pursuit-evasion game with a dynamics formulation, an evader is not able to escape from a slightly faster pursuer with an effective learned pursuit strategy, based on agile maneuvers and an effective learned evasion strategy.