This paper addresses agents' intentions as building blocks of imitation learning that abstract local situations of the agent, and proposes a hierarchical hidden Markov model ...
This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begi...
Abstract. In this paper we address the problem of prioritising feedback on the basis of multiple heterogeneous pieces of information in exploratory learning. The problem arises whe...
Acting in a dynamic environment is a complex task that requires several issues to be investigated, with the aim of controlling the associated search complexity. In this paper, a l...
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...