6 accepted papers
In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent tasks using the vast visual and physical resources available in the UE community, and deploy state-of-the-art (SOTA) MA
In parameter-efficient fine-tuning, mixture-of-experts (MoE), which involves specializing functionalities into different experts and sparsely activating them appropriately, has been widely adopted as a promising approach to trade-off between model capacity and computation overhead. However, current
When dealing with a series of imminent issues, humans can naturally concentrate on a subset of these concerning issues by prioritizing them according to their contributions to motivational indices, e.g., the probability of winning a game. This idea of concentration offers insights into reinforcement…
In this paper, we propose a novel distributed method based on attention-based deep reinforcement learning using individual reward shaping, for multiple unmanned aerial vehicles (UAVs) cooperative short-range combat mission. Specifically, a two-level attention distributed policy, composed of observat…
This paper proposes a collaborative policy framework via relational graph reasoning for multi-agent systems to accomplish adversarial tasks. A relational graph reasoning module consisting of an agent graph reasoning module and an opponent graph module, is designed to enable each agent to learn mixtu…
This paper considers a multi-target coverage problem where a robot team aims to efficiently cover multi-targets while maintaining connectivity in a distributed manner. A novel knowledge-incorporated policy framework is proposed to derive a distributed, efficient, and connectivity guaranteed coverage…