Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

no code implementations • 8 Sep 2021 • Maziar Gomrokchi, Susan Amin, Hossein Aboutalebi, Alexander Wong, Doina Precup

To address this gap, we propose an adversarial attack framework designed for testing the vulnerability of a state-of-the-art deep reinforcement learning algorithm to a membership inference attack.

Adversarial Attack Continuous Control +5

A Survey of Exploration Methods in Reinforcement Learning

no code implementations • 1 Sep 2021 • Susan Amin, Maziar Gomrokchi, Harsh Satija, Herke van Hoof, Doina Precup

Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments.

reinforcement-learning Reinforcement Learning (RL)

Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards

1 code implementation • 26 Dec 2020 • Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi, Harsh Satija, Doina Precup

A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action spaces.

Continuous Control

Predicting Regional Locust Swarm Distribution with Recurrent Neural Networks

no code implementations • 29 Nov 2020 • Hadia Mohmmed Osman Ahmed Samil, Annabelle Martin, Arnav Kumar Jain, Susan Amin, Samira Ebrahimi Kahou

Locust infestation of some regions in the world, including Africa, Asia and Middle East has become a concerning issue that can affect the health and the lives of millions of people.