تقرير
A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents
| العنوان: | A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents |
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| المؤلفون: | Bi, Yifei, Chen, Xinyi, Xiao, Caihui |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | Adapting the idea of training CartPole with Deep Q-learning agent, we are able to find a promising result that prevent the pole from falling down. The capacity of reinforcement learning (RL) to learn from the interaction between the environment and agent provides an optimal control strategy. In this paper, we aim to solve the classic pendulum swing-up problem that making the learned pendulum to be in upright position and balanced. Deep Deterministic Policy Gradient algorithm is introduced to operate over continuous action domain in this problem. Salient results of optimal pendulum are proved with increasing average return, decreasing loss, and live video in the code part. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2106.09556 |
| رقم الأكسشن: | edsarx.2106.09556 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |