We present a condition on the matrix of an underdetermined linear system which guarantees that the solution of the system with minimal q-quasinorm is also the sparsest one. This generalizes, and slightly improves, a similar result for the 1norm. We then introduce a simple numerical scheme to compute solutions with minimal q-quasinorm, and we study its convergence. Finally, we display the result...

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller...

In realistic multiagent systems, learning on the basis of complete state information is not feasible. We introduce adaptive state focus Q-learning, a class of methods derived from Qlearning that start learning with only the state information that is strictly necessary for a single agent to perform the task, and that monitor the convergence of learning. If lack of convergence is detected, the le...

This paper focuses on a multi-agent cooperation which is generally di cult to be achieved without su cient information of other agents, and proposes the reinforcement learning method that introduces an internal reward for a multi-agent cooperation without su cient information. To guarantee to achieve such a cooperation, this paper theoretically derives the condition of selecting appropriate act...

The paper discusses a multi-agent system for robot-soccer. The system consists of a supervisory controller, and controllers for attack, defense and goalie robots. Real time vector field based path planning, Petri-net theory and Q-learning technique are used in the design. Robot-soccer system has a dynamic environment. A soccer robot has to take an appropriate decision based on environment situa...