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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