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JMLR
2002
125views more  JMLR 2002»
14 years 11 months ago
Lyapunov Design for Safe Reinforcement Learning
Lyapunov design methods are used widely in control engineering to design controllers that achieve qualitative objectives, such as stabilizing a system or maintaining a system'...
Theodore J. Perkins, Andrew G. Barto
CIRA
2007
IEEE
148views Robotics» more  CIRA 2007»
15 years 6 months ago
Reinforcement Learning with a Supervisor for a Mobile Robot in a Real-world Environment
– This paper describes two experiments with supervised reinforcement learning (RL) on a real, mobile robot. Two types of experiments were preformed. One tests the robot’s relia...
Karla Conn, Richard Alan Peters II
IJCM
2008
93views more  IJCM 2008»
14 years 11 months ago
A reinforced learning control using iterative error compensation for uncertain dynamical systems
This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of high speed, computer controlled machining process. ...
Kuei-Shu Hsu, Wen-Shyong Yu, Ming-In Ho
IJCAI
2001
15 years 1 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
CORR
2011
Springer
194views Education» more  CORR 2011»
14 years 3 months ago
Accelerating Reinforcement Learning through Implicit Imitation
Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent’s ability to learn useful behaviors by making intelligent use of the kn...
Craig Boutilier, Bob Price