Changxi Zhu

Orcid: 0000-0002-2910-5506

According to our database1, Changxi Zhu authored at least 12 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2024
Correction: A survey of multi-agent deep reinforcement learning with communication.
Auton. Agents Multi Agent Syst., June, 2024

A survey of multi-agent deep reinforcement learning with communication.
Auton. Agents Multi Agent Syst., June, 2024

2023
Detecting Dependency-Related Sentiment Features for Aspect-Level Sentiment Classification.
IEEE Trans. Affect. Comput., 2023

Learning by reusing previous advice: a memory-based teacher-student framework.
Auton. Agents Multi Agent Syst., 2023

2022
A Survey of Multi-Agent Reinforcement Learning with Communication.
CoRR, 2022

A Versatile Adaptive Curriculum Learning Framework for Task-oriented Dialogue Policy Learning.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

2021
A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget Constraint.
ACM Trans. Auton. Adapt. Syst., 2021

Efficient Dialogue Complementary Policy Learning via Deep Q-network Policy and Episodic Memory Policy.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020
A Q-values Sharing Framework for Multiagent Reinforcement Learning under Budget Constraint.
CoRR, 2020

Learning by Reusing Previous Advice in Teacher-Student Paradigm.
Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

2019
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

A Q-values Sharing Framework for Multiple Independent Q-learners.
Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019


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