Enhancing Deep Reinforcement Learning Approaches for Multi-Robot Navigation via Single-Robot Evolutionary Policy Search-Reference-Cited by-同舟云学术

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Enhancing Deep Reinforcement Learning Approaches for Multi-Robot Navigation via Single-Robot Evolutionary Policy Search

Published:2022-05-23 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Robotics and Automation (ICRA)
language:
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Author:

Marchesini Enrico¹,Farinelli Alessandro¹

Affiliation:

1. University of Verona,Department of Computer Science,Verona,Italy,37135

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9811522/9811357/09812341.pdf?arnumber=9812341

Reference34 articles.

1. Formal verification of neu-ral networks for safety-critical tasks in deep reinforcement learning;corsi;UAI,2021

2. Unity: A platform for intelligent agents;juliani;CoRR,2018

3. Rainbow: Combining improvements in drl;hessel;AAAI,2018

4. Sim-to-Real Transfer for Optical Tactile Sensing

5. Formal verifi-cation for safe deep reinforcement learning in trajectory generation;corsi;IRC,2020

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1. Learning Autonomous Navigation in Unmapped and Unknown Environments;Sensors;2024-09-12

2. Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning;Electronics;2024-07-24

3. A FPGA Accelerator of Distributed A3C Algorithm with Optimal Resource Deployment;IET Computers & Digital Techniques;2024-05-27

4. Robust Motion Planning for Multi-Robot Systems Against Position Deception Attacks;IEEE Transactions on Information Forensics and Security;2024

5. Entropy Maximization in High Dimensional Multiagent State Spaces;2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS);2023-12-04

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