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ICML
2010
IEEE
13 years 8 months ago
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
We introduce new, efficient algorithms for value iteration with multiple reward functions and continuous state. We also give an algorithm for finding the set of all nondominated a...
Daniel J. Lizotte, Michael H. Bowling, Susan A. Mu...
AAAI
1997
13 years 8 months ago
Reinforcement Learning with Time
This paper steps back from the standard infinite horizon formulation of reinforcement learning problems to consider the simpler case of finite horizon problems. Although finite ho...
Daishi Harada
IJSNET
2006
145views more  IJSNET 2006»
13 years 7 months ago
RL-MAC: a reinforcement learning based MAC protocol for wireless sensor networks
:This paper introduces RL-MAC, a novel adaptive MediaAccess Control (MAC) protocol for Wireless Sensor Networks (WSN) that employs a reinforcement learning framework. Existing sche...
Zhenzhen Liu, Itamar Elhanany
ICCBR
2001
Springer
13 years 12 months ago
Meta-case-Based Reasoning: Using Functional Models to Adapt Case-Based Agents
It is useful for an intelligent software agent to be able to adapt to new demands from an environment. Such adaptation can be viewed as a redesign problem; an agent has some origin...
J. William Murdock, Ashok K. Goel
NIPS
1994
13 years 8 months ago
Generalization in Reinforcement Learning: Safely Approximating the Value Function
To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to t...
Justin A. Boyan, Andrew W. Moore