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AAAI
2008
13 years 7 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
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
IROS
2007
IEEE
164views Robotics» more  IROS 2007»
13 years 11 months ago
Emulation and behavior understanding through shared values
— Neurophysiology has revealed the existence of mirror neurons in brain of macaque monkeys and they shows similar activities during executing an observation of goal directed move...
Yasutake Takahashi, Teruyasu Kawamata, Minoru Asad...
IJCAI
2003
13 years 6 months ago
Use of Off-line Dynamic Programming for Efficient Image Interpretation
An interpretation system finds the likely mappings from portions of an image to real-world objects. An interpretation policy specifies when to apply which imaging operator, to whi...
Ramana Isukapalli, Russell Greiner
INFOCOM
2010
IEEE
13 years 3 months ago
Neighbor Discovery with Reception Status Feedback to Transmitters
—Neighbor discovery is essential for the process of self-organization of a wireless network, where almost all routing and medium access protocols need knowledge of one-hop neighb...
Ramin Khalili, Dennis Goeckel, Donald F. Towsley, ...