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CCIA
2005
Springer
15 years 10 months ago
Direct Policy Search Reinforcement Learning for Robot Control
— This paper proposes a high-level Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, whe...
Andres El-Fakdi, Marc Carreras, Narcís Palo...
EURONGI
2005
Springer
15 years 10 months ago
An Afterstates Reinforcement Learning Approach to Optimize Admission Control in Mobile Cellular Networks
We deploy a novel Reinforcement Learning optimization technique based on afterstates learning to determine the gain that can be achieved by incorporating movement prediction inform...
José Manuel Giménez-Guzmán, J...
ECML
2004
Springer
15 years 9 months ago
Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework
Given the pattern-based multi-predictors of the stock price, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset ...
Jangmin O, Jae Won Lee, Jongwoo Lee, Byoung-Tak Zh...
AIMSA
2004
Springer
15 years 8 months ago
Towards Well-Defined Multi-agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) is an emerging area of research. However, it lacks two important elements: a coherent view on MARL, and a well-defined problem objective. ...
Rinat Khoussainov
AAAI
2007
15 years 6 months ago
Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison
Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and te...
Matthew E. Taylor, Shimon Whiteson, Peter Stone