In this paper, we advocate the use of Sparse Distributed Memories (SDMs) for on-line, value-based reinforcement learning (RL). SDMs provide a linear, local function approximation scheme, designed to work when a very large/ high-dimensional input (address) space has to be mapped into a much smaller physical memory. We present an implementation of the SDM architecture for on-line, value-based RL ...

A variety of problems in digital marketing can be modeled as Markov decision processes and solved by dynamic programming with the goal of calculating the policy that maximizes the expected discounted reward. Algorithms, such as policy iteration, require a state transition and a reward model, which can be estimated based on a given data set. In this paper, we compare the execution times for esti...

We consider the design of multi-agent systems (MAS) so as to optimize an overall world utility function when each agent in the system runs a Reinforcement Learning (RL) algorithm based on own its private utility function. Traditional game theory deals with the "forward problem" of determining the state of a MAS that will ensue from a specified set of private utilities of the individual agents. ...

Recent advancements in reinforcement learning confirm that reinforcement learning techniques can solve large scale problems leading to high quality autonomous decision making. It is a matter of time until we will see large scale applications of reinforcement learning in various sectors, such as healthcare and cyber-security, among others. However, reinforcement learning can be time-consuming be...

This paper describes a new algorithm, called MDQL, for the solution of multiple objective optimization problems. MDQL is based on a new distributed Q-learning algorithm, called DQL, which is also introduced in this paper. In DQL a family of independent agents, exploring diierent options, nds a common policy in a common environment. Information about action goodness is transmitted using traces o...

Multi-agents systems (MAS) are developed using a variety of architectures nowadays. These range from peer-to-peer to blackboard-like communication, from insecure distributed systems to complex security schemes, from those MAS using database to those using knowledge based information systems. Although a system’s architecture often has to be designed according to its specific application domain, ...

Decision problems can be usually solved using systems that implement diierent paradigms. These systems may be integrated into a single distributed system, with the expectation of obtaining a group performance more satisfactory than individual performances. Such a distributed system is what we call a Multi Agent Decision System (MADES), a special kind of Multi Agent System, that integrates sever...

Consider a set of non-cooperative agents acting in an environment in which each agent attempts to maximize a private utility function. As each agent maximizes its private utility we desire a global ”world” utility function to in turn be maximized. The inverse problem induced from this situation is the following: How does each agent choose his move so that while he optimizes his private utility,...

Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a powerful modeling technique for realistic multi-agent coordination problems under uncertainty. Prevalent solution techniques are centralized and assume prior knowledge of the model. We propose a distributed reinforcement learning approach, where agents take turns to learn best responses to each other’s policies. T...

Control and performance optimization of wireless networks Unmanned Aerial Vehicles (UAVs) require scalable approaches that go beyond architectures based on centralized network controllers. At the same time, model-based is often limited by accuracy approximations relaxations necessary to solve UAV control problem through convex or similar techniques, channel models used. To address these challen...