In this paper we address the problem of learning a policy from demonstration. Assuming that the policy to be learned is the optimal policy for an underlying MDP, we propose a novel way of leveraging the underlying MDP structure in a kernel-based approach. Our proposed approach rests on the insight that the MDP structure can be encapsulated into an adequate state-space metric. In particular we s...

Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a parameterized policy space in order to maximize the associated value function averaged over some predefined distribution. The best one can hope in general from such an approach is to get a local optimum of this criterion. The first contribution of this article is ...

in the age of globalization, neoliberal developmentalism is hegemonic cherachtristic of the international system. in this age legitimacy of political systems depends on national development. in such a space, creating a constructive interaction between national and international levels is a must for sucsess in national development plans. foreign policy is the main field & major means of creating...

fields, which are needed for checking firewall policy violations, from the pattern expression of a flow rule to represent the space of corresponding flow path. In addition, we reorganize these fields with a (source address, destination address) pair to specify a flow path space. Then, we define three kinds of spaces for representing a flow path space: (1) Incoming Space represents original head...