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SOLVING BASED INTROSPECTION TO AUGMENT THE TRAINING OF REINFORCEMENT LEARNING AGENTS FOR CONTROL AND PLANNING ON ROBOTS AND AUTONOMOUS VEHICLES
SOLVING BASED INTROSPECTION TO AUGMENT THE TRAINING OF REINFORCEMENT LEARNING AGENTS FOR CONTROL AND PLANNING ON ROBOTS AND AUTONOMOUS VEHICLES
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机译:基于解决方案的内向型增强对机器人和自主车辆的控制和计划的强化学习代理的培训
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摘要
Described is a system for controlling a mobile platform. A neural network that runs on the mobile platform is trained based on a current state of the mobile platform. A Satisfiability Modulo Theories (SMT) solver capable of reasoning over non-linear activation functions is periodically queried to obtain examples of states satisfying specified constraints of the mobile platform. The neural network is then trained on the examples of states. Following training on the examples of states, the neural network selects an action to be performed by the mobile platform in its environment. Finally, the system causes the mobile platform to perform the selected action in its environment.
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