Nash Q-Learning For General-Sum Stochastic Games. This learning protocol provably conv. This learning protocol provably converges given certain restrictions on the stage.
Survey on cognitive anti‐jamming communications Aref 2020 IET Communications Wiley from ietresearch.onlinelibrary.wiley.com
Definition 3 in stochastic game f a nash equilibrium point is a tuple of n strategies (it for a such that for all s e s and i ltn) > i/ (s where it is the set of strategies available to agent i. Cannot retrieve contributors at this time. For each (s, a), q (s, a) = r (f (s, a)) + v (f.
Survey on cognitive anti‐jamming communications Aref 2020 IET Communications Wiley
For each (s, a), q (s, a) = r (f (s, a)) + v (f. Wang and sandholm describe an algorithm that converges almost surely to an optimal equilibrium in any team stochastic game. We prove that under certain conditions, by updating the entropy regularization, the algorithm Definition 3 in stochastic game f a nash equilibrium point is a tuple of n strategies (it for a such that for all s e s and i ltn) > i/ (s where it is the set of strategies available to agent i.
