Experience Weighted Learning in Multiagent Systems

Table 2

Notations.

Variables	Explanation
N (t)	The observed count of interactions in time t
	The action of an agent in time t
	The policy mapping action a to probability
	The discount rate for experience
	The decay of utility with respect to time
	The reward of action a
	The utility of taking action a