In this paper the application of reinforcement learning to Tetris is investigated, particulary the idea of temporal difference learning is applied to estimate the state value funct...
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...
Developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of compe...
We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coev...
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw...
We consider the problem of learning density mixture models for classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent t...