  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.
