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  • title: Constrained Markov Decision Processes via Backward Value Functions
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            Constrained Markov Decision Processes via Backward Value Functions
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            Constrained Markov Decision Processes via Backward Value Functions

            Jul 12, 2020

            Speakers

            HS

            Harsh Satija

            Speaker · 1 follower

            PA

            Philip Amortila

            Speaker · 0 followers

            JP

            Joelle Pineau

            Speaker · 5 followers

            About

            Although Reinforcement Learning (RL) algorithms have found tremendous success in simulated domains, they often cannot directly be applied to physical systems, especially in cases where there are hard constraints to satisfy (e.g. on safety or resources). In standard RL, the agent is incentivized to explore any behavior as long as it maximizes rewards, but in the real world, undesired behavior can damage either the system or the agent in a way that breaks the learning process itself. In this work,…

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            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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