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TECoSA Research Seminar: Explainable Reinforcement Learning for Telecom
September 29, 12:00 – 13:00
Speakers: Industrial PhD students Ahmad Terra (TECoSA, Ericsson) and Franco Ruggeri (Ericsson)
Location: C435, Brinellvägen 85 (plan 4)
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ABSTRACT: Explainable Reinforcement Learning (XRL) primarily to provide explainability for black-box models to enable trust. XRL includes multiple methods applied to different elements of RL agent (state, rewards, policy explanations, etc.). This seminar will present multiple existing state-of-the-art XRL methods, their application to different elements especially to the remote electrical antenna tilt (RET) use case. Additionally, the seminar includes a new method “Both Ends Explanations for RL (BEERL)” that goes one-step further to connect and compare input and output explanations, identify and mitigate bias, and generates two different levels of explanation which in turn allows RL agent re-configurations when unwanted behaviours are observed.