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researcher

Julia M. Kim

6 papers hereh-index 345 citations9 works total

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

author position
  • middle author5

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.AI4
  • cs.LG2

identity via Semantic Scholar / OpenAlex

activity
20222024
most citedNovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty

5 citations · 6 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2024

Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning

Jonathan C. Balloch, Rishav Bhagat, Geigh Zollicoffer +3

In deep reinforcement learning (RL) research, there has been a concerted effort to design more efficient and productive exploration methods while solving sparse-reward problems. Th…

cs.LG2022★ 1 cited

The Role of Exploration for Task Transfer in Reinforcement Learning

Jonathan C Balloch, Julia Kim, and Jessica L Inman +1

The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the…

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