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View all- Nandy ASeshathri SSarkar A(2023)Maze Solving Using Deep Q-NetworkProceedings of the 2023 6th International Conference on Advances in Robotics10.1145/3610419.3610458(1-5)Online publication date: 5-Jul-2023
Abstract
In the field of robot navigation, Path Planning is a very important problem. Reasonable Path Planning can greatly improve the efficiency of transportation and ensure the safety of robots. The traditional Path Planning method solves the problem of an optimal path to some extent, but it is far from enough. With Machine Learning becoming a hot topic, Path Planning using Reinforcement Learning and Deep Reinforcement Learning has been studied. Q-learning, as a basic algorithm of Reinforcement Learning, has been applied for a long time and has been improved by combining multiple algorithms. And Deep Q-network, a classical algorithm of Deep Reinforcement Learning, has been used to solve complex problems which traditional Reinforcement Learning cannot solve, particularly in Path Planning. This article will present the current achievements in the Improvement of Q-learning (IQL) and Deep Q-network (DQN). In the future, Reinforcement Learning and Deep Reinforcement Learning will generate more and better algorithms to solve problems with higher complexity and need shorter response times.
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Index Terms
- A Review of Path Planning Based on IQL and DQN
Index terms have been assigned to the content through auto-classification.
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October 2022
594 pages
ISBN:9781450398442
DOI:10.1145/3570773
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Published: 09 December 2022
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ISAIMS 2022: 2022 3rd International Symposium on Artificial Intelligence for Medicine Sciences
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View all- Nandy ASeshathri SSarkar A(2023)Maze Solving Using Deep Q-NetworkProceedings of the 2023 6th International Conference on Advances in Robotics10.1145/3610419.3610458(1-5)Online publication date: 5-Jul-2023
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