For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
Alexander L. Strehl, Lihong Li, Eric Wiewiora, Joh...
Enforced hill-climbing is an effective deterministic hillclimbing technique that deals with local optima using breadth-first search (a process called "basin flooding"). ...
In this paper, westudy the application of an ttMM(hidden Markov model) to the problem of representing protein sequencesby a stochastic motif. Astochastic protein motif represents ...
Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performan...
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is tha...