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» Intelligent learning and control of autonomous robotic agent...
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CEC
2005
IEEE
13 years 11 months ago
Effects of experience bias when seeding with prior results
Abstract- Seeding the population of an evolutionary algorithm with solutions from previous runs has proved to be useful when learning control strategies for agents operating in a c...
Mitchell A. Potter, R. Paul Wiegand, H. Joseph Blu...
ATAL
2010
Springer
13 years 6 months ago
Linear options
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Jonathan Sorg, Satinder P. Singh
ATAL
2009
Springer
13 years 12 months ago
An empirical analysis of value function-based and policy search reinforcement learning
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
Shivaram Kalyanakrishnan, Peter Stone
DAMAS
2005
Springer
13 years 11 months ago
Cognitive Agents for Sense and Respond Logistics
We present a novel cognitive agent architecture and demonstrate its effectiveness in the Sense and Respond Logistics (SRL) domain. Effective applications to support SRL must antic...
Kshanti A. Greene, David G. Cooper, Anna L. Buczak...
ATAL
2007
Springer
13 years 11 months ago
Batch reinforcement learning in a complex domain
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
Shivaram Kalyanakrishnan, Peter Stone