核心概念
CrossQ introduces a lightweight algorithm using Batch Normalization to improve sample efficiency in Deep RL.
摘要
The paper introduces CrossQ, a new algorithm that enhances sample efficiency in Deep RL by utilizing Batch Normalization. It compares favorably to state-of-the-art methods like REDQ and DroQ, offering improved computational efficiency and performance without relying on target networks or high UTD ratios.
Abstract:
Sample efficiency is crucial in deep reinforcement learning.
Recent algorithms like REDQ and DroQ aim to improve sample efficiency but come with increased computational costs.
CrossQ introduces a lightweight algorithm that surpasses current state-of-the-art methods while maintaining low UTD ratio of 1.
Introduction:
SAC's critic may be underfitted due to limited gradient update steps.
REDQ and DroQ increase the UTD ratio for better sample efficiency.
CrossQ removes target networks and utilizes Batch Normalization for stability and improved performance.
Data Extraction:
"Sample efficiency is a crucial problem in deep reinforcement learning."
"Recent algorithms, such as REDQ and DroQ, found a way to improve the sample efficiency."
統計資料
サンプル効率は深い強化学習における重要な問題です。
最近のアルゴリズム、例えばREDQやDroQは、サンプル効率を向上させる方法を見つけました。
引述
"Sample efficiency is a crucial problem in deep reinforcement learning."
"Recent algorithms, such as REDQ and DroQ, found a way to improve the sample efficiency."