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Malik Aqeel Anwar

2 accepted papers

2022

RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning

ICRA 2022poster

Present-day Deep Reinforcement Learning (RL) systems show great promise towards building intelligent agents surpassing human-level performance. However, the computational complexity associated with the underlying deep neural networks (DNNs) leads to power-hungry implementations. This makes deep RL s…

Cited by 5SourceScholar
2021

A decentralized policy gradient approach to multi-task reinforcement learning

UAI 2021poster

We develop a mathematical framework for solving multi-task reinforcement learning (MTRL) problems based on a type of policy gradient method. The goal in MTRL is to learn a common policy that operates effectively in different environments; these environments have similar (or overlapping) state spaces…

Cited by 51SourcePDFScholar