Entropy, Free Full-Text
Por um escritor misterioso
Last updated 07 julho 2024
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Recently, deep reinforcement learning (RL) algorithms have achieved significant progress in the multi-agent domain. However, training for increasingly complex tasks would be time-consuming and resource intensive. To alleviate this problem, efficient leveraging of historical experience is essential, which is under-explored in previous studies because most existing methods fail to achieve this goal in a continuously dynamic system owing to their complicated design. In this paper, we propose a method for knowledge reuse called “KnowRU”, which can be easily deployed in the majority of multi-agent reinforcement learning (MARL) algorithms without requiring complicated hand-coded design. We employ the knowledge distillation paradigm to transfer knowledge among agents to shorten the training phase for new tasks while improving the asymptotic performance of agents. To empirically demonstrate the robustness and effectiveness of KnowRU, we perform extensive experiments on state-of-the-art MARL algorithms in collaborative and competitive scenarios. The results show that KnowRU outperforms recently reported methods and not only successfully accelerates the training phase, but also improves the training performance, emphasizing the importance of the proposed knowledge reuse for MARL.
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Entropy, Free Full-Text
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Entropy (classical thermodynamics) - Wikipedia
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High‐Entropy Oxides: Fundamental Aspects and Electrochemical Properties - Sarkar - 2019 - Advanced Materials - Wiley Online Library
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Gibbs free energy and spontaneity (article)
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Entropy Generation Minimization - Colaboratory
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Urban Demons Version 0.8 Beta Free - Colaboratory
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ENTROPY Vol.1 Sample Pack Bundle – 16-levels
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Entropy, Free Full-Text, An Entropy-Based Design Evaluation Model for Architectural Competitions through …
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Controlling entropy to tune the functions of intrinsically disordered regions - ScienceDirect
Entropy-Free Energy 01 Answers PDF, PDF, Entropy
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Urban Demons Version 0.8 Beta Free - Colaboratory
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Hindered Translator and Hindered Rotor Models for Adsorbates: Partition Functions and Entropies
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Entropy MDPI on X: Call for reading: Axiomatic Characterization of the #Quantum Relative #Entropy and Free Energy published at / X
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Decision Trees Explained — Entropy, Information Gain, Gini Index, CCP Pruning, by Shailey Dash
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