This document summarizes a presentation on offline reinforcement learning. It discusses how offline RL can learn from fixed datasets without further interaction with the environment, which allows for fully off-policy learning. However, offline RL faces challenges from distribution shift between the behavior policy that generated the data and the learned target policy. The document reviews several
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
処理を実行中です
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く