Internal rewards mitigate agent boundedness

ICML 2010 - Proceedings, 27th International Conference on Machine Learning

Volumn , Issue , 2010, Pages 1007-1014

  Sorg, Jonathan ^a   Singh, Satinder ^a   Lewis, Richard ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGENT DESIGN; BOUNDEDNESS; FORMAL FRAMEWORK; INTERNAL AGENTS; META-OPTIMIZATION; REWARD FUNCTION;

LEARNING SYSTEMS; OPTIMIZATION;

DESIGN;

EID: 77956525933     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

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2. Ng, Andrew Y., Harada, D., and Russell, S. Policy invariance under reward transformations: Theory and application to reward shaping. In Proceedings of the 16th International conference on Machine Learning, pp. 278-287, Bled, Slovenia, 1999.
3. Singh, Satinder, Lewis, Richard L., and Barto, Andrew G. Where Do Rewards Come From? In Proceedings of the Annual Conference of the Cognitive Science Society, pp. 2601-2606, 2009.
4. Strehl, Alexander L. and Littman, Michael L. A theoretical analysis of model-based interval estimation. In Proceedings of the 22nd International Conference on Machine Learning, pp. 857-864, 2005.
5. Sutton, Richard S. and Barto, Andrew G. Reinforcement Learning: An Introduction. The MIT Press, 1998.
6. Weaver, Lex and Tao, Nigel. The Optimal Reward Baseline for Gradient-Based Reinforcement Learning. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, 2001.