226 citations · 686 across the 13 of their papers we have counts for
4 papers · 1 filter
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…
Learning Finite-State Controllers for Partially Observable Environments
Nicolas Meuleau, Leonid Peshkin, Kee-Eung Kim +1
Reactive (memoryless) policies are sufficient in completely observable Markov decision processes (MDPs), but some kind of memory is usually necessary for optimal control of a parti…
Factored Particles for Scalable Monitoring
Brenda Ng, Leonid Peshkin, Avi Pfeffer
Exact monitoring in dynamic Bayesian networks is intractable, so approximate algorithms are necessary. This paper presents a new family of approximate monitoring algorithms that co…
Learning from Scarce Experience
Leonid Peshkin, Christian R. Shelton
Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each c…