Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Xiao, Baicen"'
Reinforcement learning involves agents interacting with an environment to complete tasks. When rewards provided by the environment are sparse, agents may not receive immediate feedback on the quality of actions that they take, thereby affecting learn
Externí odkaz:
http://arxiv.org/abs/2202.09489
This paper considers multi-agent reinforcement learning (MARL) tasks where agents receive a shared global reward at the end of an episode. The delayed nature of this reward affects the ability of the agents to assess the quality of their actions at i
Externí odkaz:
http://arxiv.org/abs/2201.04612
Multi-agent reinforcement learning involves multiple agents interacting with each other and a shared environment to complete tasks. When rewards provided by the environment are sparse, agents may not receive immediate feedback on the quality of actio
Externí odkaz:
http://arxiv.org/abs/2103.15941
This paper studies the control of safety-critical dynamical systems in the presence of adversarial disturbances. We seek to synthesize state-feedback controllers to minimize a cost incurred due to the disturbance, while respecting a safety constraint
Externí odkaz:
http://arxiv.org/abs/2009.09511
Autor:
Xiao, Baicen, Lu, Qifan, Ramasubramanian, Bhaskar, Clark, Andrew, Bushnell, Linda, Poovendran, Radha
Reinforcement learning has been successful in training autonomous agents to accomplish goals in complex environments. Although this has been adapted to multiple settings, including robotics and computer games, human players often find it easier to ob
Externí odkaz:
http://arxiv.org/abs/2001.06781
Autor:
Xiao, Baicen, Ramasubramanian, Bhaskar, Clark, Andrew, Hajishirzi, Hannaneh, Bushnell, Linda, Poovendran, Radha
This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies. We show that a potential-based reward shaping scheme is able to preserve optim
Externí odkaz:
http://arxiv.org/abs/1907.08823
It is known that humans display "shape bias" when classifying new items, i.e., they prefer to categorize objects based on their shape rather than color. Convolutional Neural Networks (CNNs) are also designed to take into account the spatial structure
Externí odkaz:
http://arxiv.org/abs/1803.07739
Due to the growth of video data on Internet, automatic video analysis has gained a lot of attention from academia as well as companies such as Facebook, Twitter and Google. In this paper, we examine the robustness of video analysis algorithms in adve
Externí odkaz:
http://arxiv.org/abs/1708.04301
Google has recently introduced the Cloud Vision API for image analysis. According to the demonstration website, the API "quickly classifies images into thousands of categories, detects individual objects and faces within images, and finds and reads p
Externí odkaz:
http://arxiv.org/abs/1704.05051
Despite the rapid progress of the techniques for image classification, video annotation has remained a challenging task. Automated video annotation would be a breakthrough technology, enabling users to search within the videos. Recently, Google intro
Externí odkaz:
http://arxiv.org/abs/1703.09793