arXiv:2606.03963v1 Announce Type: cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks. However, its practical use still depends heavily on human designed reward functions and repeated manual fine tuning, which is time consuming and does not guarantee high success in the desired task. This paper presents AgenticRL, agent guided reinforcement learning framework that increases autonomy in reward design, policy refinement, and real world deployment for unmanned aerial vehicles (UAV) navigation tasks. AgenticRL us
The increasing complexity of AI tasks and the limitations of human supervision are driving research into more autonomous and efficient reinforcement learning paradigms.
This framework significantly reduces manual intervention in AI training and deployment, accelerating the development and real-world application of autonomous systems, especially in environments like UAV navigation.
The reliance on human-designed reward functions and manual tuning for complex robotic tasks is diminished, leading to more self-sufficient AI development processes.
- · Autonomous systems developers
- · Logistics and delivery sectors
- · Defense and security contractors
- · Robotics research institutions
- · Manual drone operation services
- · Traditional AI model fine-tuning specialists
More robust and adaptable autonomous UAVs are developed for diverse applications.
Reduced operational costs and faster deployment cycles for drone-based services.
Enhanced AI 'self-awareness' leading to broader agentic applications beyond navigation, potentially impacting other sectors.
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Read at arXiv cs.AI