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Action advising endeavors to leverage supplementary guidance from expertteachers to alleviate the issue of sampling inefficiency in Deep ReinforcementLearning (DRL). Previous agent-specific action advising methods are hindered byimperfections in the agent itself, while agent-agnostic approaches exhibitlimited adaptability to the learning agent. In this study, we propose a novelframework called Agent-Aware trAining yet Agent-Agnostic Action Advising (A7)to strike a balance between the two. The underlying concept of A7 revolvesaround utilizing the similarity of state features as an indicator forsoliciting advice. However, unlike prior methodologies, the measurement ofstate feature similarity is performed by neither the error-prone learning agentnor the agent-agnostic advisor. Instead, we employ a proxy model to extractstate features that are both discriminative (adaptive to the agent) andgenerally applicable (robust to agent noise). Furthermore, we utilize behaviorcloning to train a model for reusing advice and introduce an intrinsic rewardfor the advised samples to incentivize the utilization of expert guidance.Experiments are conducted on the GridWorld, LunarLander, and six prominentscenarios from Atari games. The results demonstrate that A7 significantlyaccelerates the learning process and surpasses existing methods (bothagent-specific and agent-agnostic) by a substantial margin. Our code will bemade publicly available.

Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning