Chenhao Yao

Chenhao Yao

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Paper Title Number 2

Published in IEEE TII, 2025

Multiagent formation obstacle avoidance is a crucial research topic in the field of multiagent cooperative control, and deep reinforcement learning has shown remarkable potential in this domain. However, most existing studies are not fully distributed and often involve relatively simple scenarios. In this article, we propose an advanced method based on multiagent deep reinforcement learning to address formation and obstacle avoidance in dynamic obstacles environments. For handling complex environments with an unknown number of obstacles, we use long short-term memory (LSTM) networks to encode dynamic obstacles, thereby improving the efficiency of obstacle avoidance. Our method achieves formation and obstacle avoidance in scenarios with both dynamic and static obstacles, where agents coordinate through fully independent and autonomous decision-making. We utilize the multiagent proximal policy optimization (MAPPO) algorithm for centralized training and distributed execution, enhancing the agents’ formation and obstacle avoidance capabilities in complex settings. Through simulation and real-world experiments, and by comparing with benchmark methods, we demonstrate significant improvements in formation effectiveness and obstacle avoidance success rates, showcasing the superiority and practicality of our proposed approach.

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Application of LLM Guided Reinforcement Learning in Formation Control with Collision Avoidance

Published in IROS, 2025

Multi-Agent Systems (MAS) excel at accomplish- ing complex objectives through the collaborative efforts of individual agents. Among the methodologies employed in MAS, Multi-Agent Reinforcement Learning (MARL) stands out as one of the most efficacious algorithms. However, when con- fronted with the complex objective of Formation Control with Collision Avoidance (FCCA): designing an effective reward function that facilitates swift convergence of the policy network to an optimal solution. In this paper, we introduce a novel framework that aims to overcome this challenge. By giving large language models (LLMs) on the prioritization of tasks and the observable information available to each agent, our framework generates reward functions that can be dynamically adjusted online based on evaluation outcomes by employing more advanced evaluation metrics rather than the rewards themselves. This mechanism enables the MAS to simultaneously achieve formation control and obstacle avoidance in dynamic environments with enhanced efficiency, requiring fewer iter- ations to reach superior performance levels. Our empirical studies, conducted in both simulation and real-world settings, validate the practicality and effectiveness of our proposed approach.

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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