Difference between revisions of "Using Deep Q-Learning (Alpha-Go algorithm) to solve routing problems in warehouse logistics"
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{{StudentProjectTemplate | {{StudentProjectTemplate | ||
− | |Summary= | + | |Summary=Solving the famous vehicle-routing problem using Deep Q-Learning |
|Keywords=vehicle routing problem, Reinforcement learning | |Keywords=vehicle routing problem, Reinforcement learning | ||
|Supervisor=Jennifer David, Thorsteinn Rögnvaldsson, | |Supervisor=Jennifer David, Thorsteinn Rögnvaldsson, | ||
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|Status=Open | |Status=Open | ||
}} | }} | ||
+ | For a group of robots or forklifts involved in warehouses, Sony Mobile in Lund has a Masters Thesis project. The aim is to generate paths/routes for all vehicles according to a pre-determined schedule without any path-conflicts and schedule conflicts. This is an NP-hard problem termed as vehicle routing problem and has been solved using heuristic approaches. Here, we try to use Deep Q learning to solve it. |
Latest revision as of 17:00, 19 October 2018
Title | Using Deep Q-Learning (Alpha-Go algorithm) to solve routing problems in warehouse logistics |
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Summary | Solving the famous vehicle-routing problem using Deep Q-Learning |
Keywords | vehicle routing problem, Reinforcement learning |
TimeFrame | |
References | |
Prerequisites | |
Author | |
Supervisor | Jennifer David, Thorsteinn Rögnvaldsson |
Level | Master |
Status | Open |
For a group of robots or forklifts involved in warehouses, Sony Mobile in Lund has a Masters Thesis project. The aim is to generate paths/routes for all vehicles according to a pre-determined schedule without any path-conflicts and schedule conflicts. This is an NP-hard problem termed as vehicle routing problem and has been solved using heuristic approaches. Here, we try to use Deep Q learning to solve it.