226 citations · 1.1k across the 10 of their papers we have counts for
10 papers
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…
A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains
Nicolas Meuleau, Emmanuel Benazera, Ronen I. Brafman +2
We consider the problem of optimal planning in stochastic domains with resource constraints, where the resources are continuous and the choice of action at each step depends on res…
Hierarchical Solution of Markov Decision Processes using Macro-actions
Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kaelbling +2
We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and…
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…
Solving POMDPs by Searching the Space of Finite Policies
Nicolas Meuleau, Kee-Eung Kim, Leslie Pack Kaelbling +1
Solving partially observable Markov decision processes (POMDPs) is highly intractable in general, at least in part because the optimal policy may be infinitely large. In this paper…
Planning under Continuous Time and Resource Uncertainty: A Challenge for AI
John Bresina, Richard Dearden, Nicolas Meuleau +3
We outline a class of problems, typical of Mars rover operations, that are problematic for current methods of planning under uncertainty. The existing methods fail because they suf…