In this paper, we propose a deep reinforcement learning algorithm to learn multiple tasks
concurrently. A new network architecture is proposed in the algorithm which reduces the …

Sepsis is a life-threatening condition with a high in-hospital mortality rate. The timing of
antibiotic administration poses a critical problem for sepsis management. Existing work …

The ability to leverage shared behaviors between tasks is critical for sample-efficient multi-
task reinforcement learning (MTRL). While prior methods have primarily explored parameter …

Reinforcement learning has shown great potential in generalizing over raw sensory data
using only a single neural network for value optimization. There are several challenges in …

Reinforcement learning is a broad scheme of learning algorithms that, in recent times, has
shown astonishing performance in controlling agents in environments presented as Markov …

Deep reinforcement learning has greatly improved the performance of learning agent by
combining the strong generalization and extraction ability of deep learning models with the …