Wolfpack attack framework disrupts MARL cooperation by targeting initial and assisting agents; WALL trains robust policies against it with reported experimental gains.
arXiv preprint arXiv:1712.03632 , year=
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abstract
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks. We show that even a naively engineered attack successfully degrades the performance of DRL algorithm. We further improve the attack using gradient information of an engineered loss function which leads to further degradation in performance. These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep Double Q learning and Deep Deterministic Policy Gradients leads to significant increase in robustness to parameter variations for RL benchmarks such as Cart-pole, Mountain Car, Hopper and Half Cheetah environment.
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Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Wolfpack attack framework disrupts MARL cooperation by targeting initial and assisting agents; WALL trains robust policies against it with reported experimental gains.
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