Michail G. Lagoudakis

Learn More
We propose a new approach to reinforcement learning for control problems which combines value-function approximation with linear architectures and approximate policy iteration. This new approach is motivated by the least-squares temporal-difference learning algorithm (LSTD) for prediction problems, which is known for its efficient use of sample experiences(More)
Recently, auction methods have been investigated as effective, decentralized methods for multi-robot coordination. Experimental research has shown great potential, but has not been complemented yet by theoretical analysis. In this paper we contribute a theoretical analysis of the performance of auction methods for multi-robot routing. We suggest a generic(More)
The basic tools of machine learning appear in the inner loop of most reinforcement learning algorithms, typically in the form of Monte Carlo methods or function approximation techniques. To a large extent, however, current reinforcement learning algorithms draw upon machine learning techniques that are at least ten years old and, with a few exceptions, very(More)
Teams of robots are more fault tolerant than single robots, and auctions appear to be promising means for coordinating them. In a recent paper at “Robotics: Science and Systems 2005,” we analyzed a coordination system based on sequential single-item auctions. We showed that the coordination system is simple to implement and computation and communication(More)
We consider the problem of allocating a number of exploration tasks to a team of mobile robots. Each task consists of a target location that needs to be visited by a robot. The objective of the allocation is to minimize the total cost, that is, the sum of the travel costs of all robots for visiting all targets. We show that finding an optimal allocation is(More)
Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an(More)
Robotics researchers have used auction-based coordination systems for robot teams because of their robustness and efficiency. However, there is no research into systematic methods for deriving appropriate bidding rules for given team objectives. In this paper, we propose the first such method and demonstrate it by deriving bidding rules for three possible(More)
Least-squares methods have been successfully used for prediction problems in the context of reinforcement learning, but little has been done in extending these methods to control problems. This paper presents an overview of our research efforts in using least-squares techniques for control. In our early attempts, we considered a direct extension of the(More)