An Empirical Investigation of Early Stopping Optimizations in Proximal Policy Optimization
Code-level optimizations, which are low-level optimization techniques used in the implementation of algorithms, have generally been considered as tangential and often do not appear in published pseudo-code of Reinforcement Learning (RL) algorithms. However, recent studies suggest these optimizations...
Main Authors: | Rousslan Fernand Julien Dossa, Shengyi Huang, Santiago Ontanon, Takashi Matsubara |
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Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9520424/ |
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