Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Paruchuri, Praveen"'
Preserving the privacy of preferences (or rewards) of a sequential decision-making agent when decisions are observable is crucial in many physical and cybersecurity domains. For instance, in wildlife monitoring, agents must allocate patrolling resour
Externí odkaz:
http://arxiv.org/abs/2407.09809
Autor:
Moodley, Perusha, Kaushik, Pramod, Thambi, Dhillu, Trovinger, Mark, Paruchuri, Praveen, Hong, Xia, Rosman, Benjamin
Decision Transformers, in their vanilla form, struggle to perform on image-based environments with multi-discrete action spaces. Although enhanced Decision Transformer architectures have been developed to improve performance, these methods have not s
Externí odkaz:
http://arxiv.org/abs/2407.01310
In safety-critical RL settings, the inclusion of an additional cost function is often favoured over the arduous task of modifying the reward function to ensure the agent's safe behaviour. However, designing or evaluating such a cost function can be p
Externí odkaz:
http://arxiv.org/abs/2406.19626
A significant cause of air pollution in urban areas worldwide is the high volume of road traffic. Long-term exposure to severe pollution can cause serious health issues. One approach towards tackling this problem is to design a pollution-aware traffi
Externí odkaz:
http://arxiv.org/abs/2302.14442
Moving Target Defense (MTD) has emerged as a key technique in various security applications as it takes away the attacker's ability to perform reconnaissance for exploiting a system's vulnerabilities. However, most of the existing research in the fie
Externí odkaz:
http://arxiv.org/abs/2301.09892
Periodic double auctions (PDA) have applications in many areas such as in e-commerce, intra-day equity markets, and day-ahead energy markets in smart-grids. While the trades accomplished using PDAs are worth trillions of dollars, finding a reliable b
Externí odkaz:
http://arxiv.org/abs/2201.10127
Reinforcement Learning (RL) enables agents to learn how to perform various tasks from scratch. In domains like autonomous driving, recommendation systems, and more, optimal RL policies learned could cause a privacy breach if the policies memorize any
Externí odkaz:
http://arxiv.org/abs/2112.05495
Periodic Double Auctions (PDAs) are commonly used in the real world for trading, e.g. in stock markets to determine stock opening prices, and energy markets to trade energy in order to balance net demand in smart grids, involving trillions of dollars
Externí odkaz:
http://arxiv.org/abs/1911.08260
Publikováno v:
In Transportation Research Part D February 2020 79