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researcher

Jun Ki Lee

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.HC1

identity via Semantic Scholar / OpenAlex

most citedDeep Reinforcement Learning from Policy-Dependent Human Feedback

31 citations · 31 across the 2 of their papers we have counts for

collaborators

4 papers

cs.HC2019

Stackelberg Punishment and Bully-Proofing Autonomous Vehicles

Matt Cooper, Jun Ki Lee, Jacob Beck +7

Mutually beneficial behavior in repeated games can be enforced via the threat of punishment, as enshrined in game theory's well-known "folk theorem." There is a cost, however, to a…

cs.LG2019★ 31 cited

Deep Reinforcement Learning from Policy-Dependent Human Feedback

Dilip Arumugam, Jun Ki Lee, Sophie Saskin +1

To widen their accessibility and increase their utility, intelligent agents must be able to learn complex behaviors as specified by (non-expert) human users. Moreover, they will ne…

cs.LG2018

Measuring and Characterizing Generalization in Deep Reinforcement Learning

Sam Witty, Jun Ki Lee, Emma Tosch +3

Deep reinforcement-learning methods have achieved remarkable performance on challenging control tasks. Observations of the resulting behavior give the impression that the agent has…

cs.LG2018

Mitigating Planner Overfitting in Model-Based Reinforcement Learning

Dilip Arumugam, David Abel, Kavosh Asadi +5

An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is opti…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.