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Santiago Miret

5 papers hereh-index 4159 citations6 works total

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

author position
  • first author2
  • middle author3

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

fields
  • cs.LG4
  • cs.NE1
same name
  • Santiago Miret — 10 papers, h 12
  • Santiago Miret — 10 papers, h 6
  • Santiago Miret — 6 papers, h 5
  • Santiago Miret — 5 papers, h 3
  • Santiago Miret — 4 papers, h 4
  • Santiago Miret — 2 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192021
most citedCollaborative Evolutionary Reinforcement Learning

32 citations · 35 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2020★ 1 cited

Learning Intrinsic Symbolic Rewards in Reinforcement Learning

Hassam Sheikh, Shauharda Khadka, Santiago Miret +1

Learning effective policies for sparse objectives is a key challenge in Deep Reinforcement Learning (RL). A common approach is to design task-related dense rewards to improve task…

cs.LG2020

Safety Aware Reinforcement Learning (SARL)

Santiago Miret, Somdeb Majumdar, Carroll Wainwright

As reinforcement learning agents become increasingly integrated into complex, real-world environments, designing for safety becomes a critical consideration. We specifically focus…

cs.LG2020★ 2 cited

Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning

Shauharda Khadka, Estelle Aflalo, Mattias Marder +6

For deep neural network accelerators, memory movement is both energetically expensive and can bound computation. Therefore, optimal mapping of tensors to memory hierarchies is crit…

cs.LG2019★ 32 cited

Collaborative Evolutionary Reinforcement Learning

Shauharda Khadka, Somdeb Majumdar, Tarek Nassar +5

Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically struggle with achieving effective ex…

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