We identify two fundamental points of utilizing CBR for an adaptive agent that tries to learn on the basis of trial and error without a model of its environment. The first link co...
Decision-theoretic models have become increasingly popular as a basis for solving agent and multiagent problems, due to their ability to quantify the complex uncertainty and prefe...
- We address the issues of improving the feature generation methods for the value-function approximation and the state space approximation. We focus the improvement of feature gene...
A wireless system with multiple channels is considered, where each channel has several transmission states. A user learns about the instantaneous state of an available channel by ...
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elegant solution to the exploration-exploitation trade-off in reinforcement learning...