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Cody Wild

2 accepted papers

2021

An Empirical Investigation of Representation Learning for Imitation

NeurIPS 2021poster

Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment. However, collecting expert demonstrations can be expensive. Recent work in vision, reinforcement learning, and NLP has shown that auxiliary r…

Cited by 33SourceScholar
2020

Adversarial Policies: Attacking Deep Reinforcement Learning

ICLR 2020poster

Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent's observations. This might lead one to wonder: is it pos…

Cited by 472SourcecodeScholar