We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
When its human operator cannot continuously supervise (much less teleoperate) an agent, the agent should be able to recognize its limitations and ask for help when it risks making...
Robert Cohn, Michael Maxim, Edmund H. Durfee, Sati...
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...
Abstract--Cooperative communications have been demonstrated to be effective in combating the multiple fading effects in wireless networks, and improving the network performance in ...
Xuedong Liang, Ilangko Balasingham, Victor C. M. L...
In bandit problems, a decision-maker must choose between a set of alternatives, each of which has a fixed but unknown rate of reward, to maximize their total number of rewards ov...
Michael D. Lee, Shunan Zhang, Miles Munro, Mark St...