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Mobilized ad-hoc networks: A reinforcement learning approach
(2003-12-04)
Research in mobile ad-hoc networks has focused on situations in whichnodes have no control over their movements. We investigate animportant but overlooked domain in which nodes do have controlover their movements. ...
Importance Sampling for Reinforcement Learning with Multiple Objectives
(2001-08-01)
This thesis considers three complications that arise from applying reinforcement learning to a real-world application. In the process of using reinforcement learning to build an adaptive electronic market-maker, we find ...
Mobilized ad-hoc networks: A reinforcement learning approach
(2003-12-04)
Research in mobile ad-hoc networks has focused on situations in which nodes have no control over their movements. We investigate an important but overlooked domain in which nodes do have control over their movements. ...
Reinforcement Learning by Policy Search
(2003-02-14)
One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially ...
A Reinforcement-Learning Approach to Power Management
(2002-05-01)
We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our ...
Modeling Stock Order Flows and Learning Market-Making from Data
(2002-06-01)
Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel method for modeling ...