Simple Rule Extra tion : The Extra tion of PlanningKnowledge from Reinfor ement

Countering Deception in Multiagent Reinforcement Learning

ABSTRACT Multiagent Reinfor ement Learning (MRL) is a growing area of resear h. What makes it parti ularly hallenging is the non-stationarity of the target fun tion. Most of the existing work in this area, however, address either stationary environments or self-play. We assume an asymmetri and non-stationary environment where other agents an be of arbitrary dispositions. In parti ular, agents a...

Beyond Simple Rule Extra tion: A quiring Planning Knowledge from Neural Networks

Extra tion of Rules from Arti ial

Networks for Nonlinear Regression Rudy Setiono S hool of Computing National University of Singapore Kent Ridge, Singapore 119260 Wee Kheng Leow S hool of Computing National University of Singapore Kent Ridge, Singapore 119260 Ja ek M. Zurada Dept. of Ele tri al and Computer Engineering University of Louisville Louisville, KY 40208, USA Abstra t Neural networks have been su essfully applied to s...

Learning Agents in a Homo Egualis Society

ABSTRACT Coordination is an important issue in multi-agent systems. A possible approa h to ta kle oordination, that re ently reeived quite a lot of attention, is to learn the e e ts of intera tion in the joint a tion spa e. However joint a tion spa es violate generally a epted requirements of multi-agents systems, su h as : distributed ontrol, asyn hronous a tions, in omplete information, ost o...

Autonomous agent learning using an actor-critic algorithm and behavior models

We introdu e a Supervised Reinfor ement Learning (SRL) algorithm for autonomous learning problems where an agent is required to deal with high dimensional spa es. In our learning algorithm, behavior models learned from a set of examples, are used to dynami ally redu e the set of relevant a tions at ea h state of the environment en ountered by the agent. Su h subsets of a tions are used to guide...