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Human-level control through deep learning and deep reinforcement learning have revealed the unique and powerful potentials through a very complex Go game. The AlphaGo, developed by Google DeepMind, has beat the top Go game player early this year. The scientific and technological advancement behind the success of AlphaGo attracted researchers from multiple areas, including machine learning, artificial...
In adaptive control, agents interacting with Markov Decision Processes typically face two types of setups. In the first setup, the environment's model is known and dynamic programming and related methods are used to obtain the optimal control. In the second setup, the environment's model is unknown and reinforcement learning methods are used. In this work we investigate a new setup that is a mix of...
Multi-stage decision problems under uncertainty are abundant in process industries. Markov decision process (MDP) is a general mathematical formulation of such problems. Whereas stochastic programming and dynamic programming are the standard methods to solve MDPs, their unwieldy computational requirements limit their usefulness in real applications. Approximate dynamic programming (ADP) combines simulation...
This paper presents an approximate policy iteration algorithm for solving infinite-horizon, discounted Markov decision processes (MDPs) for which a model of the system is available. The algorithm is similar in spirit to Bellman residual minimization methods. However, by using Gaussian process regression with nondegenerate kernel functions as the underlying cost-to-go function approximation architecture,...
Viola and Jones (VJ) cascade classification methods have proven to be very successful in detecting objects belonging to a single class - e.g., faces. This paper addresses the more challenging "many class detection" problem: detecting and identifying objects that belong to any of a set of classes. We use a set of learned weights (corresponding to the parameters of a set of binary linear separators)...
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