Overcoming Exploration: Deep Reinforcement Learning in Complex Environments from Temporal Logic Specifications

• URL: http://arxiv.org/abs/2201.12231v2
• Date: Tue, 1 Feb 2022 17:35:07 GMT
• Title: Overcoming Exploration: Deep Reinforcement Learning in Complex Environments from Temporal Logic Specifications
• Authors: Mingyu Cai, Erfan Aasi, Calin Belta, Cristian-Ioan Vasile
• Abstract summary: We present a Deep Reinforcement Learning (DRL) algorithm for a task-guided robot with unknown continuous-time dynamics deployed in a large-scale complex environment. Our framework is shown to significantly improve performance (effectiveness, efficiency) and exploration of robots tasked with complex missions in large-scale complex environments.
• Score: 2.8904578737516764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a Deep Reinforcement Learning (DRL) algorithm for a task-guided robot with unknown continuous-time dynamics deployed in a large-scale complex environment. Linear Temporal Logic (LTL) is applied to express a rich robotic specification. To overcome the environmental challenge, we propose a novel path planning-guided reward scheme that is dense over the state space, and crucially, robust to infeasibility of computed geometric paths due to the unknown robot dynamics. To facilitate LTL satisfaction, our approach decomposes the LTL mission into sub-tasks that are solved using distributed DRL, where the sub-tasks are trained in parallel, using Deep Policy Gradient algorithms. Our framework is shown to significantly improve performance (effectiveness, efficiency) and exploration of robots tasked with complex missions in large-scale complex environments.

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