Search Results for author: Sunil Mallya

Found 8 papers, 1 papers with code

Robust Multi-Agent Reinforcement Learning with Model Uncertainty

no code implementations NeurIPS 2020 Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar

In contrast, we model the problem as a robust Markov game, where the goal of all agents is to find policies such that no agent has the incentive to deviate, i. e., reach some equilibrium point, which is also robust to the possible uncertainty of the MARL model.

Multi-agent Reinforcement Learning Q-Learning +1

REPAINT: Knowledge Transfer in Deep Reinforcement Learning

no code implementations24 Nov 2020 Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya

Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low.

reinforcement-learning Transfer Learning

REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning

no code implementations28 Sep 2020 Yunzhe Tao, Sahika Genc, Tao Sun, Sunil Mallya

Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low or unknown.

reinforcement-learning Transfer Learning

Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks

no code implementations2 Jan 2020 Sahika Genc, Sunil Mallya, Sravan Bodapati, Tao Sun, Yunzhe Tao

Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem.

Autonomous Driving Deep Attention +3

Effectiveness of LSTMs in Predicting Congestive Heart Failure Onset

no code implementations7 Feb 2019 Sunil Mallya, Marc Overhage, Navneet Srivastava, Tatsuya Arai, Cole Erdman

In this paper we present a Recurrent neural networks (RNN) based architecture that achieves an AUCROC of 0. 9147 for predicting the onset of Congestive Heart Failure (CHF) 15 months in advance using a 12-month observation window on a large cohort of 216, 394 patients.

Feature Importance Medical Diagnosis

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