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- W3121342653 abstract "Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. In this work, we identify and formalize a series of independent challenges that embody the difficulties that must be addressed for RL to be commonly deployed in real-world systems. For each challenge, we define it formally in the context of a Markov Decision Process, analyze the effects of the challenge on state-of-the-art learning algorithms, and present some existing attempts at tackling it. We believe that an approach that addresses our set of proposed challenges would be readily deployable in a large number of real world problems. Our proposed challenges are implemented in a suite of continuous control environments called realworldrl-suite which we propose an as an open-source benchmark." @default.
- W3121342653 created "2021-02-01" @default.
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- W3121342653 date "2021-04-22" @default.
- W3121342653 modified "2023-10-05" @default.
- W3121342653 title "Challenges of real-world reinforcement learning: definitions, benchmarks and analysis" @default.
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- W3121342653 doi "https://doi.org/10.1007/s10994-021-05961-4" @default.
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