PubMed · 4723555
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
E Pfaffelhuber. 1973. Mathematical learning models and neuronal networks.. https://doi.org/10.1016/0022-5193(73)90165-3
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Explore the source record for details and available documents.
We exhibit an important property called the asymptotic equipartition property (AEP) on empirical sequences in an ergodic multiagent Markov decision process (MDP). Using the AEP which facilitates the analysis of multiagent learning, we give a statistical property of multiagent learning, such as reinforcement learning (RL), near the end of the learning process. We examine the effect of the conditions among the agents on the achievement of a cooperative policy in three different cases: blind, visible, and communicable. Also, we derive a bound on the speed with which the empirical sequence converges to the best sequence in probability, so that the multiagent learning yields the best cooperative result.
The well known Cohen-Grossberg network is modified to include second order neural interconnections and also to have a learning component. Sufficient conditions are obtained for the existence of a globally exponentially stable equilibrium. The model provides a two-fold generalization of the Cohen-Grossberg network in the sense if one removes the learning component, then one gets a network with second order synaptic interactions; if both the learning component and the second order interactions are removed, then the model reduces to the standard Cohen-Grossberg network.