Decomposing Synthesized Strategies for Reactive Multi-agent Reinforcement Learning

Zhu, Chenyang; Zhu, Jing; Cai, Yujie; Wang, Fang

doi:10.1007/978-3-031-35257-7_4

Cited by 1 publication

(2 citation statements)

References 26 publications

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“…In the context of multi-agent RL, cooperative task decomposition combined with learning reward machines [17] and individual reward machine decomposition for enhanced learning efficiency [7] display promising potential. However, these approaches should be evaluated in complex multi-agent scenarios in order to determine their resilience, scalability, and potential shortcomings.…”

Section: Related Workmentioning

confidence: 99%

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Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation

Zhou,

Shang,

2023

Electronics

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Hierarchical reinforcement learning (HRL) offers a hierarchical structure for organizing tasks, enabling agents to learn and make decisions autonomously in complex environments. However, traditional HRL approaches face limitations in effectively handling complex tasks. Reward machines, which specify high-level goals and associated rewards for sub-goals, have been introduced to address these limitations by facilitating the agent’s understanding and reasoning with respect to the task hierarchy. In this paper, we propose a novel approach to enhance HRL performance through topologically sorted potential calculation for reward machines. By leveraging the topological structure of the task hierarchy, our method efficiently determines potentials for different sub-goals. This topological sorting enables the agent to prioritize actions leading to the accomplishment of higher-level goals, enhancing the learning process. To assess the efficacy of our approach, we conducted experiments in the grid-world environment with OpenAI-Gym. The results showcase the superiority of our proposed method over traditional HRL techniques and reward machine-based reinforcement learning approaches in terms of learning efficiency and overall task performance.

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Section: Related Workmentioning

confidence: 99%

“…This hierarchical approach enhances exploration and learning within complex environments while maintaining interpretability and adaptability. Moreover, reward machines empower agents to master non-Markovian properties, enabling them to retain information about previously accomplished sub-tasks [6,7].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation

Zhou,

Shang,

2023

Electronics

View full text Add to dashboard Cite

show abstract

Decomposing Synthesized Strategies for Reactive Multi-agent Reinforcement Learning

Cited by 1 publication

References 26 publications

Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation

Enhancing Efficiency in Hierarchical Reinforcement Learning through Topological-Sorted Potential Calculation

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