Real world multiagent coordination problems are important issues for reinforcement learning techniques. In general, these problems are partially observable and this characteristic ...
We study the problem of agents locating other agents that are both capable and willing to help complete assigned tasks. An agent incurs a fixed cost for each help request it sends...
We describe an effort to train a RoboCup soccer-playing agent playing in the Simulation League using casebased reasoning. The agent learns (builds a case base) by observing the be...
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that...
Imitation learning, also called learning by watching or programming by demonstration, has emerged as a means of accelerating many reinforcement learning tasks. Previous work has s...