[AI Seminar] Online AI Seminar on May 26 (Zoom) -- Thodoris Lykouris -- Corruption robust exploration in episodic reinforcement learning. AI seminar is sponsored by Fortive.

[Aayush Bansal]

Thodoris Lykouris (Microsoft Research, NYC) will be giving an online
seminar on "Corruption robust exploration in episodic reinforcement learning"
from *12:00 - 01:00 PM* on May 26.

Zoom Link: *https://cmu.zoom.us/j/262225154
<https://cmu.zoom.us/j/262225154>*

CMU AI Seminar is sponsored by Fortive.

Following are the details of the talk:

*Title: *Corruption robust exploration in episodic reinforcement learning

*Abstract: *We initiate the study of multi-stage episodic reinforcement
learning under adversarial corruptions in both the rewards and the
transition probabilities of the underlying system extending recent results
for the special case of stochastic bandits. We provide a framework which
modifies the aggressive exploration enjoyed by existing reinforcement
learning approaches based on "optimism in the face of uncertainty", by
complementing them with principles from "action elimination". Importantly,
our framework circumvents the major challenges posed by naively applying
action elimination in the RL setting, as formalized by a lower bound we
demonstrate. Our framework yields efficient algorithms which (a) attain
near-optimal regret in the absence of corruptions and (b) adapt to unknown
levels corruption, enjoying regret guarantees which degrade gracefully in
the total corruption encountered. To showcase the generality of our
approach, we derive results for both tabular settings (where states and
actions are finite) as well as linear-function-approximation settings
(where the dynamics and rewards admit a linear underlying representation).
Notably, our work provides the first sublinear regret guarantee which
accommodates any deviation from purely i.i.d. transitions in the
bandit-feedback model for episodic reinforcement learning.

*Bio*: Thodoris Lykouris is a postdoctoral researcher in the machine
learning group of Microsoft Research NYC. His research focus is on online
decision-making spanning across the disciplines of machine learning,
theoretical computer science, operations research, and economics. He
completed his Ph.D. in 2019 from Cornell University where he was advised by
Eva Tardos. During his Ph.D. years, his research has been generously
supported by a Google Ph.D. Fellowship and a Cornell University Fellowship.
He was also a finalist in the INFORMS Nicholson and Applied Probability
Society best student paper competitions.


To learn more about the seminar series, please visit the website:
http://www.cs.cmu.edu/~aiseminar/


-- 
Aayush Bansal
http://www.cs.cmu.edu/~aayushb/
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