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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Behavior fusion for deep reinforcement learning
ترجمه فارسی عنوان مقاله:
همجوشی رفتار برای یادگیری تقویت عمیق
منبع:
Sciencedirect - Elsevier - ISA Transactions, 98 (2020) 434-444. doi:10.1016/j.isatra.2019.08.054
نویسنده:
Haobin Shi a, Meng Xu a, Kao-Shing Hwang b,∗,1, Bo-Yin Cai b
چکیده انگلیسی:
For deep reinforcement learning (DRL) system, it is difficult to design a reward function for complex
tasks, so this paper proposes a framework of behavior fusion for the actor–critic architecture, which
learns the policy based on an advantage function that consists of two value functions. Firstly, the
proposed method decomposes a complex task into several sub-tasks, and merges the trained policies
for those sub-tasks into a unified policy for the complex task, instead of designing a new reward
function and training for the policy. Each sub-task is trained individually by an actor–critic algorithm
using a simple reward function. These pre-trained sub-tasks are building blocks that are used to
rapidly assemble a rapid prototype of a complicated task. Secondly, the proposed method integrates
modules in the calculation of the policy gradient by calculating the accumulated returns to reduce
variation. Thirdly, two alternative methods to acquire integrated returns for the complicated task are
also proposed. The Atari 2600 pong game and a wafer probe task are used to validate the performance
of the proposed methods by comparison with the method using a gate network.
Keywords: Deep reinforcement learning | Actor–critic | Policy gradient | Behavior fusion | Complex task
قیمت: رایگان
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