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...
PAC-MDP algorithms approach the exploration-exploitation problem of reinforcement learning agents in an effective way which guarantees that with high probability, the algorithm pe...
In multihop wireless sensor networks that are often characterized by many-to-one (convergecast) traffic patterns, problems related to energy imbalance among sensors often appear. S...
This paper presents a new technique called motion balance filtering, which corrects an unbalanced motion to a balanced one while preserving the original motion characteristics as ...
We study the problem of estimating the best k term Fourier representation for a given frequency-sparse signal (i.e., vector) A of length N k. More explicitly, we investigate how t...