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个人简介
Reinforcement Learning (RL) is a learning paradigm that highly resembles how we, as humans, learn. The agent, i.e., the learning algorithm, learns through interaction with the environment. By observing the current state of the system, it decides what action to take, after which the environment transitions to a new state and produces the agent with a reward. The goal of the agent is to maximize the accumulative reward.
In my research I focus on deep reinforcement learning (DRL), where we use deep learning techniques (neural networks) to solve reinforcement learning problems.
Specifically, I am interested in finding the problems that are unique to DRL (e.g., which occur due to non-linear function approximation) and how they can be solved or mitigated in order to improve empirical performance.
研究兴趣
论文共 20 篇作者统计合作学者相似作者
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arxiv(2024)
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PROCEEDINGS OF SIGGRAPH 2023 CONFERENCE PAPERS, SIGGRAPH 2023 (2023): 37:1-37:9
AAMASpp.2430-2432, (2022)
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CoRRno. 2 (2021): 12615-12621
Machine Learningno. 9 (2021): 2295-2334
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 (2021): 8454-8463
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user-6144298de55422cecdaf68a5(2020)
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D-Core
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