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CLC number: TP181
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-05-19
Cited: 0
Clicked: 1638
Citations: Bibtex RefMan EndNote GB/T7714
Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning
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Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU. Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(11): 1541-1556.
@article{title="Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning",
author="Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="11",
pages="1541-1556",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300084"
}
%0 Journal Article
%T Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning
%A Shihmin WANG
%A Binqi ZHAO
%A Zhengfeng ZHANG
%A Junping ZHANG
%A Jian PU
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 11
%P 1541-1556
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300084
TY - JOUR
T1 - Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning
A1 - Shihmin WANG
A1 - Binqi ZHAO
A1 - Zhengfeng ZHANG
A1 - Junping ZHANG
A1 - Jian PU
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 11
SP - 1541
EP - 1556
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300084
Abstract: As one of the most fundamental topics in reinforcement learning (RL), sample efficiency is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the environment. Specifically, we first employ clustering methods in the policy sampling process to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policy data, off-policy data, and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way, our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective sampling. In particular, on the LGSVL task, our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore, our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.
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