Abstract. This paper will discuss the internal architecture for an agent framework called DECAF (Distributed Environment Centered Agent Framework). DECAF is a software toolkit for ...
This paper introduces an approach to automatic basis function construction for Hierarchical Reinforcement Learning (HRL) tasks. We describe some considerations that arise when con...
This paper presents a community of communicating embodied agents which learn an adjacency-based grammar from user interactions. The agents act as intelligent fridge magnets, each ...
The options framework provides a method for reinforcement learning agents to build new high-level skills. However, since options are usually learned in the same state space as the...
We study a multiagent learning problem where agents can either learn via repeated interactions, or can follow the advice of a mediator who suggests possible actions to take. We pr...