Explanation-Aware Experience Replay in Rule-Dense Environments
| Autor: | Francesco Sovrano, Alex Raymond, Amanda Prorok |
|---|---|
| Přispěvatelé: | Sovrano, Francesco, Raymond, Alex, Prorok, Amanda |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Control and Optimization Computer Science - Artificial Intelligence I.2.6 Mechanical Engineering Biomedical Engineering I.2.9 Computer Science Applications Machine Learning (cs.LG) Human-Computer Interaction Computer Science - Robotics Artificial Intelligence (cs.AI) Autonomous vehicle navigation experience replay explanations reinforcement learning Artificial Intelligence Control and Systems Engineering Computer Vision and Pattern Recognition Robotics (cs.RO) |
| Popis: | Human environments are often regulated by explicit and complex rulesets. Integrating Reinforcement Learning (RL) agents into such environments motivates the development of learning mechanisms that perform well in rule-dense and exception-ridden environments such as autonomous driving on regulated roads. In this paper, we propose a method for organising experience by means of partitioning the experience buffer into clusters labelled on a per-explanation basis. We present discrete and continuous navigation environments compatible with modular rulesets and 9 learning tasks. For environments with explainable rulesets, we convert rule-based explanations into case-based explanations by allocating state-transitions into clusters labelled with explanations. This allows us to sample experiences in a curricular and task-oriented manner, focusing on the rarity, importance, and meaning of events. We label this concept Explanation-Awareness (XA). We perform XA experience replay (XAER) with intra and inter-cluster prioritisation, and introduce XA-compatible versions of DQN, TD3, and SAC. Performance is consistently superior with XA versions of those algorithms, compared to traditional Prioritised Experience Replay baselines, indicating that explanation engineering can be used in lieu of reward engineering for environments with explainable features. To appear in IEEE Robotics and Automation Letters (IEEE RA-L). Please cite the published version |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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