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» Evolution of reward functions for reinforcement learning
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ICML
1999
IEEE
14 years 6 months ago
Least-Squares Temporal Difference Learning
Excerpted from: Boyan, Justin. Learning Evaluation Functions for Global Optimization. Ph.D. thesis, Carnegie Mellon University, August 1998. (Available as Technical Report CMU-CS-...
Justin A. Boyan
AIIDE
2008
13 years 7 months ago
Constructing Complex NPC Behavior via Multi-Objective Neuroevolution
It is difficult to discover effective behavior for NPCs automatically. For instance, evolutionary methods can learn sophisticated behaviors based on a single objective, but realis...
Jacob Schrum, Risto Miikkulainen
IJCAI
2007
13 years 6 months ago
Using Linear Programming for Bayesian Exploration in Markov Decision Processes
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
Pablo Samuel Castro, Doina Precup
JETAI
2002
69views more  JETAI 2002»
13 years 5 months ago
The interaction of representations and planning objectives for decision-theoretic planning tasks
We study decision-theoretic planning or reinforcement learning in the presence of traps such as steep slopes for outdoor robots or staircases for indoor robots. In this case, achi...
Sven Koenig, Yaxin Liu
IJRR
2008
139views more  IJRR 2008»
13 years 5 months ago
Learning to Control in Operational Space
One of the most general frameworks for phrasing control problems for complex, redundant robots is operational space control. However, while this framework is of essential importan...
Jan Peters, Stefan Schaal