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Mastering Atari 2600 Games with Discovered Options

Paper recorded by Signals 4 on 2026-10-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.LG · 机器学习 · first seen 2026-10-05

Abstract

Temporal abstractions, often instantiated as options, have long been regarded as a mechanism for accelerating credit assignment, facilitating exploration, and enabling generalisation in reinforcement learning (RL). However, developing general option discovery methods that are effective in large-scale, high-dimensional domains remains a fundamental challenge. Existing option discovery methods are e

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#399 most recent of 762 cs.LG papers we have recorded · ↑ newer: UniIntervene++: An Adaptive Intervention Agent for Efficient Real-Worl · ↓ older: A Path Integral Surrogate for Multi-Step Gradient Inversion in Federat
Cite this page: Mastering Atari 2600 Games with Discovered Options: the #399 most recent of 762 cs.LG papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/mastering-atari-2600-games-with-discovered-options.html
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