モデルベース制御とマルチエージェント強化学習の融合による協調チーム戦略
Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies
マルチエージェント強化学習とモデル予測制御を組み合わせ、安全で動的に実現可能な協調行動を学習する枠組みを提案。追跡回避シナリオやドローンとローバーの異種協調タスクで有効性を実証した。
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著者: Christian Llanes, Spencer W. Jensen, Samuel Coogan
分類: cs.RO, cs.LG, cs.MA
原文アブストラクト
In this work, we propose a framework that combines multi-agent reinforcement learning (MARL) with model-based control to achieve safe, dynamically feasible actions in cooperative multi-agent tasks. Multi-agent reinforcement learning provides the advantage of learning cooperative policies for multi-agent teams from discrete non-differentiable rewards in a long planning horizon. Model-predictive control is robust and offers safe, dynamically feasible actions in a fast replanning framework for short horizons. We propose an algorithm that extends actor-critic model predictive control for MARL which we refer to as multi-agent actor-critic model predictive control (MA-AC-MPC). We demonstrate the capabilities of this algorithm by applying it to a multi-agent pursuit-evasion scenario. Specifically, we compare the evader team's strategy using the MA-AC-MPC model and a multi-layer perceptron model (MA-AC-MLP). The pursuer team uses augmented proportional navigation as it is accepted as an advanced adversarial control law. We also provide an example with a heterogeneous environment where a drone and omni-wheeled rover cooperate to achieve repeatable and successful landing with 100% success rate in hardware for MA-AC-MPC compared to 60% for MA-AC-MLP. We demonstrate the robustness of the proposed MA-AC-MPC algorithm in hardware for both environments.
