Autonomous agents in snake game via deep reinforcement learning
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, similar approaches may still need to be improved when applied to more challengi...
محفوظ في:
المؤلفون الرئيسيون: | WEI, Zhepei, WANG, Di, ZHANG, Ming, TAN, Ah-hwee, MIAO, Chunyan, ZHOU, You |
---|---|
التنسيق: | text |
اللغة: | English |
منشور في: |
Institutional Knowledge at Singapore Management University
2018
|
الموضوعات: | |
الوصول للمادة أونلاين: | https://ink.library.smu.edu.sg/sis_research/6073 https://ink.library.smu.edu.sg/context/sis_research/article/7076/viewcontent/ICA2018SnakeGame.pdf |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
المؤسسة: | Singapore Management University |
اللغة: | English |
مواد مشابهة
-
Autonomous agents in snake game via deep reinforcement learning
بواسطة: Wei, Zhepei, وآخرون
منشور في: (2019) -
Deep reinforcement learning for autonomous cyber operation
بواسطة: Yong, Hou Zhong
منشور في: (2024) -
Hindsight-Combined and Hindsight-Prioritized Experience Replay
بواسطة: Tan, Renzo Roel P, وآخرون
منشور في: (2020) -
Transferable deep reinforcement learning framework for autonomous vehicles with joint radar-data communications
بواسطة: Nguyen, Quang Hieu, وآخرون
منشور في: (2023) -
Towards Explaining Sequences of Actions in Multi-Agent Deep Reinforcement Learning Models
بواسطة: KHAING, Phyo Wai, وآخرون
منشور في: (2023)