Imitating Opponent To Win: Adversarial Policy Imitation Learning In Two-player Competitive Games

Abstract

Recent research on vulnerabilities of deep reinforcement learning (RL) has shown that adversarial policies adopted by an adversary agent can influence a target RL agent (victim agent) to perform poorly in a multi-agent environment. In existing studies, adversarial policies are directly trained based on experiences of interacting with the victim agent. There is a key shortcoming of this approach; knowledge derived from historical interactions may not be properly generalized to unexplored policy regions of the victim agent, making the trained adversarial policy significantly less effective. In this work, we design a new effective adversarial policy learning algorithm that overcomes this shortcoming. The core idea of our new algorithm is to create a new imitator to imitate the victim agent's policy while the adversarial policy will be trained not only based on interactions with the victim agent but also based on feedback from the imitator to forecast victim's intention. By doing so, we ca

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keybui2022imitating

Related papers

• Adversarial Policies: Attacking Deep Reinforcement Learning (2019)0.00
• Toward Evaluating Robustness Of Reinforcement Learning With Adversarial Policy (2023)4.52
• Preventing Imitation Learning With Adversarial Policy Ensembles (2020)0.00
• A New Framework For Query Efficient Active Imitation Learning (2019)0.00
• Mimicking To Dominate: Imitation Learning Strategies For Success In Multiagent Competitive Games (2023)0.00
• Robust Deep Reinforcement Learning Against Adversarial Behavior Manipulation (2024)0.00
• Adversarial Soft Advantage Fitting: Imitation Learning Without Policy Optimization (2020)0.00
• Neutral Agent-based Adversarial Policy Learning Against Deep Reinforcement Learning In Multi-party Open Systems (2025)0.00