Safe Exploration for Identifying Linear Systems via Robust Optimization

30 Nov 2017Tyler LuMartin ZinkevichCraig BoutilierBinz RoyDale Schuurmans

Safely exploring an unknown dynamical system is critical to the deployment of reinforcement learning (RL) in physical systems where failures may have catastrophic consequences. In scenarios where one knows little about the dynamics, diverse transition data covering relevant regions of state-action space is needed to apply either model-based or model-free RL... (read more)

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