226 citations · 686 across the 13 of their papers we have counts for
4 papers · 1 filter
Reinforcement Learning for Adaptive Routing
Leonid Peshkin, Virginia Savova
Reinforcement learning means learning a policy--a mapping of observations into actions--based on feedback from the environment. The learning can be viewed as browsing a set of poli…
Learning to Cooperate via Policy Search
Leonid Peshkin, Kee-Eung Kim, Nicolas Meuleau +1
Cooperative games are those in which both agents share the same payoff structure. Value-based reinforcement-learning algorithms, such as variants of Q-learning, have been applied t…
Bounds on sample size for policy evaluation in Markov environments
Leonid Peshkin, Sayan Mukherjee
Reinforcement learning means finding the optimal course of action in Markovian environments without knowledge of the environment's dynamics. Stochastic optimization algorithms used…
Learning Policies with External Memory
Leonid Peshkin, Nicolas Meuleau, Leslie Kaelbling
In order for an agent to perform well in partially observable domains, it is usually necessary for actions to depend on the history of observations. In this paper, we explore a {\i…