Single-agent reinforcement learners in time-extended domains and multi-agent systems share a common dilemma known as the credit assignment problem. Multi-agent systems have the st...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
We have implemented an aspect of learning and memory in the nervous system using analog electronics. Using a simple synaptic circuit we realize networks with Hebbian type adaptati...
As applications for artificially intelligent agents increase in complexity we can no longer rely on clever heuristics and hand-tuned behaviors to develop their programming. Even t...
Shawn Arseneau, Wei Sun, Changpeng Zhao, Jeremy R....
The problem of interest is how to dynamically allocate wireless access services in a competitive market which implements a take-it-or-leave-it allocation mechanism. In this paper ...
George Lee, Steven Bauer, Peyman Faratin, John Wro...