Safe Exploration in Finite Markov Decision Processes with Gaussian Processes

In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications, such as robotics, where even a single unsafe action may cause system failure... (read more)

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