We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). O...
As robots become a mass consumer product, they will need to learn new skills by interacting with typical human users. Past approaches have adapted reinforcement learning (RL) to a...
We consider a bandit problem which involves sequential sampling from two populations (arms). Each arm produces a noisy reward realization which depends on an observable random cov...
Software systems built by composing existing services are more and more capturing the interest of researchers and practitioners. The envisaged long term scenario is that services, ...
Elisabetta Di Nitto, Massimiliano Di Penta, Alessi...
The optimal decision for an agent to make at a given game situation often depends on the decisions that other agents make at the same time. Rational agents will try to find a stabl...