arXiv:2606.04492v1 Announce Type: new Abstract: Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks. While episodic memory mechanisms mitigate these issues by reusing high-return trajectories, they often trap agents in local optima due to unconstrained incentive distribution and semantic representation collapse. To address this, we propose Episodic Memory Temporal Consistency (EMTC), a framework that robustly constructs and selectively leverages historical experiences. EMTC introduces two synergistic components: (1) a
The continuous evolution of AI research frequently brings new advancements in reinforcement learning, with multi-agent systems being a current frontier for developing more sophisticated autonomous behaviors.
Improved cooperative multi-agent reinforcement learning directly addresses challenges in developing robust autonomous AI systems, which are foundational for many advanced AI applications.
The proposed EMTC framework enhances multi-agent learning by addressing common issues like reward sparsity and local optima, leading to more effective and reliable AI agents.
- · AI researchers
- · Robotics industry
- · Logistics and automation companies
- · AI agent developers
- · Inefficient multi-agent reinforcement learning methods
- · Systems heavily reliant on dense reward signals
More efficient and capable multi-agent AI systems become viable for complex tasks.
Accelerated development and deployment of autonomous systems in various industries, from logistics to defense.
Increased public and industrial adoption of AI agents for coordinating complex, real-world operations.
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Read at arXiv cs.LG