In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinfor...In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.展开更多
This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accu...This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.展开更多
文摘In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate a smaller "aggregate" Markov decision problem, whose states relate to the features. We discuss properties and possible implementations of this type of aggregation, including a new approach to approximate policy iteration. In this approach the policy improvement operation combines feature-based aggregation with feature construction using deep neural networks or other calculations. We argue that the cost function of a policy may be approximated much more accurately by the nonlinear function of the features provided by aggregation, than by the linear function of the features provided by neural networkbased reinforcement learning, thereby potentially leading to more effective policy improvement.
文摘This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.
