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This paper proposes a method that combines imperfect simulators with limited real-world data to address the challenges of learning effective reinforcement learning (RL) policies for complex real-world tasks. The Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning (H2O) framework adjusts Q-function learning by considering dynamic differences and performs training on a fixed real-world dataset. We demonstrate the superiority of H2O through simulations, real-world tasks, and theoretical analysis, presenting a novel hybrid offline-online RL paradigm.
Takeaways, Limitations
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Takeaways:
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Improving the performance of RL policy learning in real-world environments by combining limited real-world data and imperfect simulators.
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Bridging the gap between simulation and reality through policy evaluation methods that take dynamic differences into account.
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Demonstrated excellent performance in various simulations and real-world tasks.
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Contributes to future RL algorithm design by presenting a new hybrid RL paradigm.
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Limitations:
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The specific performance and limitations of H2O depend on the experiments and analyses presented.
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In actual application, performance may vary depending on the degree of incompleteness of the simulator and the amount of actual data.
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Beyond comparisons with other RL algorithms, further research is needed on H2O's generalization ability.
