
Learning from Demonstrations (LfD) refers to using expert demonstrations combined with the reward information given by the environment to jointly guide the learning of policy in Reinforcement Learning. Previous LfD methods usually assume that provided demonstrations are perfect. While in real-world applications, demonstrations are often collected from multiple sources, which may contain imperfect ones. In this work, we aim to deal with the latter situation, i.e., Learning from Imperfect Demonstrations (LfID), where demonstrations only include trajectories with state-action pairs. To this end, two challenges need to be solved: evaluation for the demonstrations and calibration for the bonus model. Both challenges can be more severe in sparse reward environments, since the exploration problem will appear while learning. In this work, we focus on bridging the exploration and LfID problems in view of anomaly detection, and further proposing AGPO method to deal with these problems. Compared with state-of-the-art methods, empirical studies on some continuous control benchmarks show the superiority of AGPO in this scenario.

Anomaly Guided Policy Learning from Imperfect Demonstrations


**Session Chair:** <br><br> Mark Reynolds

Oral Session 3B6-2

technical paper

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

Zi-Xuan Chen

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Anomaly Guided Policy Learning from Imperfect Demonstrations

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