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Pursuit-evasion games are ubiquitous in nature and in an artificial world. Innature, pursuer(s) and evader(s) are intelligent agents that can learn fromexperience, and dynamics (i.e., Newtonian or Lagrangian) is vital for thepursuer and the evader in some scenarios. To this end, this paper addresses thepursuit-evasion game of intelligent agents from the perspective of dynamics. Abio-inspired dynamics formulation of a pursuit-evasion game and baselinepursuit and evasion strategies are introduced at first. Then, reinforcementlearning techniques are used to mimic the ability of intelligent agents tolearn from experience. Based on the dynamics formulation and reinforcementlearning techniques, the effects of improving both pursuit and evasionstrategies based on experience on pursuit-evasion games are investigated at twolevels 1) individual runs and 2) ranges of the parameters of pursuit-evasiongames. Results of the investigation are consistent with nature observations andthe natural law - survival of the fittest. More importantly, with respect tothe result of a pursuit-evasion game of agents with baseline strategies, thisstudy achieves a different result. It is shown that, in a pursuit-evasion gamewith a dynamics formulation, an evader is not able to escape from a slightlyfaster pursuer with an effective learned pursuit strategy, based on agilemaneuvers and an effective learned evasion strategy.

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