We have recently introduced an incremental learning algorithm, Learn++ .NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Le...
– 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...
— This paper describes a panoramic view-based navigation in outdoor environments. We have been developing a two-phase navigation method. In the training phase, the robot acquires...
Hideo Morita, Michael Hild, Jun Miura, Yoshiaki Sh...
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
We pose partitioning a b-bit Internet Protocol (IP) address space as a supervised learning task. Given (IP, property) labeled training data, we develop an IP-specific clustering a...