To measure the distance between two states in different MDPs, we extend Fern's metric of states [10], which considers actions, prob- ability transition functions and reward functions of those states. Then we apply Kantorovich metric and Hausdorff metric to measure the distance between the two state sets of two MDPs.
In this paper, we propose two metrics for measuring the distance between finite MDPs. Our metrics are based on the Hausdorff metric which measures the distance ...
Two metrics based on the Hausdorff metric and the Kantorovich metric are proposed which can be used to compute the distance between reinforcement learning ...
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Jul 11, 2012 · We present metrics for measuring the similarity of states in a finite Markov decision process (MDP). The formulation of our metrics is based on the notion of ...
We present metrics for measuring the similarity of states in a finite Markov decision process (MDP). The formulation of our metrics is based on the notion ...
In this paper we address the same problem in a differ- ent way, by developing metrics, or distance functions, on the states of an MDP. Unlike an equivalence ...
Markov decision processes (MDPs) offer a popular math- ematical tool for planning and learning in the presence of uncertainty (Boutilier, Dean, & Hanks ...
We present metrics for measuring state similarity in Markov decision processes (MDPs) with infinitely many states, including MDPs with continuous state spaces.
Oct 14, 2022 · Markov decision processes (MDPs) are a common way of encoding decision-making ... Measuring the distance between finite markov decision processes.
In this paper, we use techniques from network optimization and statistical sampling to overcome this problem. We obtain in this manner a variety of distance ...