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» Algorithm Selection using Reinforcement Learning
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AGI
2008
15 years 4 months ago
An Integrative Methodology for Teaching Embodied Non-Linguistic Agents, Applied to Virtual Animals in Second Life
A teaching methodology called Imitative-Reinforcement-Corrective (IRC) learning is described, and proposed as a general approach for teaching embodied non-linguistic AGI systems. I...
Ben Goertzel, Cassio Pennachin, Nil Geisweiller, M...
SIGDIAL
2010
15 years 1 months ago
Sparse Approximate Dynamic Programming for Dialog Management
Spoken dialogue management strategy optimization by means of Reinforcement Learning (RL) is now part of the state of the art. Yet, there is still a clear mismatch between the comp...
Senthilkumar Chandramohan, Matthieu Geist, Olivier...
ICML
2010
IEEE
15 years 4 months ago
Internal Rewards Mitigate Agent Boundedness
Abstract--Reinforcement learning (RL) research typically develops algorithms for helping an RL agent best achieve its goals-however they came to be defined--while ignoring the rela...
Jonathan Sorg, Satinder P. Singh, Richard Lewis
AGENTS
1999
Springer
15 years 7 months ago
General Principles of Learning-Based Multi-Agent Systems
We consider the problem of how to design large decentralized multiagent systems (MAS’s) in an automated fashion, with little or no hand-tuning. Our approach has each agent run a...
David Wolpert, Kevin R. Wheeler, Kagan Tumer
COLT
2010
Springer
15 years 1 months ago
Robust Selective Sampling from Single and Multiple Teachers
We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of ou...
Ofer Dekel, Claudio Gentile, Karthik Sridharan