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Metrics for Finite Markov Decision Processes
We present metrics for measuring the similarity of states in a finite Ma...
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Scalable methods for computing state similarity in deterministic Markov Decision Processes
We present new algorithms for computing and approximating bisimulation m...
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Efficient Planning in Large MDPs with Weak Linear Function Approximation
Large-scale Markov decision processes (MDPs) require planning algorithms...
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An Approximate Solution Method for Large Risk-Averse Markov Decision Processes
Stochastic domains often involve risk-averse decision makers. While rece...
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Stability-Constrained Markov Decision Processes Using MPC
In this paper, we consider solving discounted Markov Decision Processes ...
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Max-Plus Matching Pursuit for Deterministic Markov Decision Processes
We consider deterministic Markov decision processes (MDPs) and apply max...
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Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes
We present a method for solving implicit (factored) Markov decision proc...
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Metrics for Markov Decision Processes with Infinite State Spaces
We present metrics for measuring state similarity in Markov decision processes (MDPs) with infinitely many states, including MDPs with continuous state spaces. Such metrics provide a stable quantitative analogue of the notion of bisimulation for MDPs, and are suitable for use in MDP approximation. We show that the optimal value function associated with a discounted infinite horizon planning task varies continuously with respect to our metric distances.
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