Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Subramanian, Jayakumar"'
Autor:
Khurana, Varun, Singla, Yaman K, Subramanian, Jayakumar, Shah, Rajiv Ratn, Chen, Changyou, Xu, Zhiqiang, Krishnamurthy, Balaji
The last few years have witnessed great success on image generation, which has crossed the acceptance thresholds of aesthetics, making it directly applicable to personal and commercial applications. However, images, especially in marketing and advert
Externí odkaz:
http://arxiv.org/abs/2311.10995
In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with other teams. The dynamics and the costs are coupled through the empir
Externí odkaz:
http://arxiv.org/abs/2310.12282
As Reinforcement Learning (RL) agents are increasingly employed in diverse decision-making problems using reward preferences, it becomes important to ensure that policies learned by these frameworks in mapping observations to a probability distributi
Externí odkaz:
http://arxiv.org/abs/2307.13192
Autor:
Verma, Sukriti, Chopra, Ayush, Subramanian, Jayakumar, Sarkar, Mausoom, Puri, Nikaash, Gupta, Piyush, Krishnamurthy, Balaji
The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but since the critic learns faster than the actor, it ensures eventual cons
Externí odkaz:
http://arxiv.org/abs/2306.16503
Autor:
Deshmukh, Shripad Vilasrao, Dasgupta, Arpan, Krishnamurthy, Balaji, Jiang, Nan, Agarwal, Chirag, Theocharous, Georgios, Subramanian, Jayakumar
Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by saliency attribution to the features of the RL agent's state. In this
Externí odkaz:
http://arxiv.org/abs/2305.04073
Autor:
Chopra, Ayush, Rodríguez, Alexander, Subramanian, Jayakumar, Quera-Bofarull, Arnau, Krishnamurthy, Balaji, Prakash, B. Aditya, Raskar, Ramesh
Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments. Agent-based models (ABMs) are an increasingly popular simulation pa
Externí odkaz:
http://arxiv.org/abs/2207.09714
Autor:
Badjatiya, Pinkesh, Sarkar, Mausoom, Puri, Nikaash, Subramanian, Jayakumar, Sinha, Abhishek, Singh, Siddharth, Krishnamurthy, Balaji
Individual rationality, which involves maximizing expected individual returns, does not always lead to high-utility individual or group outcomes in multi-agent problems. For instance, in multi-agent social dilemmas, Reinforcement Learning (RL) agents
Externí odkaz:
http://arxiv.org/abs/2111.11692
Autor:
Chopra, Ayush, Gel, Esma, Subramanian, Jayakumar, Krishnamurthy, Balaji, Romero-Brufau, Santiago, Pasupathy, Kalyan S., Kingsley, Thomas C., Raskar, Ramesh
We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent populations. Using DeepABM allows scaling simulations to large agent
Externí odkaz:
http://arxiv.org/abs/2110.04421
Machine learning has successfully framed many sequential decision making problems as either supervised prediction, or optimal decision-making policy identification via reinforcement learning. In data-constrained offline settings, both approaches may
Externí odkaz:
http://arxiv.org/abs/2110.04186