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ICML
1989
IEEE
14 years 22 days ago
Uncertainty Based Selection of Learning Experiences
The training experiences needed by a learning system may be selected by either an external agent or the system itself. We show that knowledge of the current state of the learner&#...
Paul D. Scott, Shaul Markovitch
ICMLA
2009
13 years 6 months ago
Multiagent Transfer Learning via Assignment-Based Decomposition
We describe a system that successfully transfers value function knowledge across multiple subdomains of realtime strategy games in the context of multiagent reinforcement learning....
Scott Proper, Prasad Tadepalli
ICCBR
2010
Springer
14 years 17 days ago
Imitating Inscrutable Enemies: Learning from Stochastic Policy Observation, Retrieval and Reuse
In this paper we study the topic of CBR systems learning from observations in which those observations can be represented as stochastic policies. We describe a general framework wh...
Kellen Gillespie, Justin Karneeb, Stephen Lee-Urba...
AAAI
2010
13 years 9 months ago
Bayesian Policy Search for Multi-Agent Role Discovery
Bayesian inference is an appealing approach for leveraging prior knowledge in reinforcement learning (RL). In this paper we describe an algorithm for discovering different classes...
Aaron Wilson, Alan Fern, Prasad Tadepalli
IAT
2003
IEEE
14 years 1 months ago
SIMPLE - A Multi-Agent System for Simultaneous and Related Auctions
This work describes a multi-agent architecture and strategy for trade in simultaneous and related auctions. The proposed SIMPLE Agency combines an integer programming model, machi...
Ruy Luiz Milidiú, Taciana Melcop, Frederico...