限られたシミュレーション訓練下での捜索救助UAVミッションにおける目標条件付き強化学習のためのルールベース高レベルコーチング
Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
固定ルールの高レベルアドバイザとオンライン学習する低レベル強化学習コントローラを組み合わせた階層型意思決定フレームワークを提案し、限られた訓練環境でも早期の安全性とサンプル効率を向上させる。
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著者: Mahya Ramezani, Holger Voos
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios under limited simulation training. The framework combines a fixed rule-based high-level advisor with an online goal-conditioned low-level reinforcement learning (RL) controller. To stress-test early adaptation, we also consider a strict no-pretraining deployment regime. The high-level advisor is defined offline from a structured task specification and compiled into deterministic rules. It provides interpretable mission- and safety-aware guidance through recommended actions, avoided actions, and regime-dependent arbitration weights. The low-level controller learns online from task-defined dense rewards and reuses experience through a mode-aware prioritized replay mechanism augmented with rule-derived metadata. We evaluate the framework on two tasks: battery-aware multi-goal delivery and moving-target delivery in obstacle-rich environments. Across both tasks, the proposed method improves early safety and sample efficiency primarily by reducing collision terminations, while preserving the ability to adapt online to scenario-specific dynamics.
関連論文
- AgilePE: 自己対戦強化学習による自律UAV追跡・回避強化学習/UAV
