We consider the problem of policy learning in aMarkov Decision Process (MDP) where only a restricted, limited subset of the full policy space can be used. A MDP consists of a state space S, a set of actions A, a transition probability function t(s, a, s′) and a reward function R : S → R. Also there is the discount factor γ. The problem is to find a policy, a mapping from states to actions π : S...

Processor scheduling policies can be broadly divided into space-sharing and time-sharing policies. Space-sharing policies partition system processors and each partition is allocated exclusively to a job. In time-sharing policies, processors are temporally shared by jobs (e.g., in a round robin fashion). Space-sharing policies can be either static (processor allocation remains constant during th...

Reinforcement learning in environments with many action-state pairs is challenging. At issue is the number of episodes needed to thoroughly search the policy space. Most conventional heuristics address this search problem in a stochastic manner. This can leave large portions of the policy space unvisited during the early training stages. In this paper, we propose an uncertainty-based, informati...