[DL輪読会]深層強化学習はなぜ難しいのか？Why Deep RL fails? A brief survey of recent works.

Jan 15, 20213 likes3,610 views

Deep reinforcement learning algorithms often fail to learn complex tasks. Recent works have identified three issues that form a "deadly triad" contributing to this problem: non-stationary targets, high variance, and positive correlation. New algorithms aim to address these issues by improving exploration, stabilizing learning, and decorrelating updates. Overall, deep reinforcement learning remains a challenging area with opportunities to develop more data-efficient and generally applicable algorithms.

[DL輪読会]深層強化学習はなぜ難しいのか？Why Deep RL fails? A brief survey of recent works.

SSII2021 [OS2-01] 転移学習の基礎：異なるタスクの知識を利用するための機械学習の方法 6月10日 (木) 11:00 - 12:30 メイン会場（vimeo + sli.do）登壇者：松井孝太氏（名古屋大学）概要：転移学習とは、解きたいタスクに対して、それと異なるが似ている他のタスクからの知識（データ、特徴、モデルなど）を利用するための方法を与える機械学習のフレームワークです。深層モデルの学習方法として広く普及している事前学習モデルの利用は、この広義の転移学習の一つの実現形態とみなせます。本発表では、まず何をいつ転移するのか (what/when to transfer) といった転移学習の基本概念と定式化を説明し、具体的な転移学習の主要なアプローチとしてドメイン適応、メタ学習について解説します。

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