RESEARCH PAPERYear 2024

Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning

Gautham Vasan; Yan Wang; Fahim Shahriar; James Bergstra; Martin Jägersand; A. Rupam Mahmood

Classification

Major category: Foundational work
Quadrant: Not applicable
Architecture: Not applicable
Prediction paradigm: Not applicable
Subcategories: Action policy foundations Training optimization & distillation
Source review status: Verified from primary sources

Category review. This pre-2026 empirical RL study examines sparse reward and reset-timeout design for model-free goal-reaching. It is policy-learning background without a learned world predictor or canonical model component. Reading evidence

AT A GLANCE

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BibTeX

@article{vasan2024revisiting,
  author       = {Gautham Vasan and
                  Yan Wang and
                  Fahim Shahriar and
                  James Bergstra and
                  Martin J{\"{a}}gersand and
                  A. Rupam Mahmood},
  title        = {Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning},
  journal      = {{RLJ}},
  volume       = {4},
  pages        = {1841--1854},
  year         = {2024},
  
}

Classification

Contribution

Abstract

Affiliations

BibTeX

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