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A Strategic Learning Algorithm for State-based Games. (arXiv:1809.05797v1 [math.OC])
来源于:arXiv
Learning algorithm design for state-based games is investigated. A heuristic
uncoupled learning algorithm, which is a two memory better reply with inertia
dynamics, is proposed. Under certain reasonable conditions it is proved that
for any initial state, if all agents in the state-based game follow the
proposed learning algorithm, the action state pair converges almost surely to
an action invariant set of recurrent state equilibria. The design relies on
global and local searches with finite memory, inertia, and randomness. Finally,
existence of time-efficient universal learning algorithm is studied. A class of
state-based games is presented to show that there is no universal learning
algorithm converging to a recurrent state equilibrium. 查看全文>>