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Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations | IEEE Conference Publication | IEEE Xplore

Deep Imitative Reinforcement Learning for Temporal Logic Robot Motion Planning with Noisy Semantic Observations


Abstract:

In this paper, we propose a Deep Imitative Q-learning (DIQL) method to synthesize control policies for mobile robots that need to satisfy Linear Temporal Logic (LTL) spec...Show More

Abstract:

In this paper, we propose a Deep Imitative Q-learning (DIQL) method to synthesize control policies for mobile robots that need to satisfy Linear Temporal Logic (LTL) specifications using noisy semantic observations of their surroundings. The robot sensing error is modeled using probabilistic labels defined over the states of a Labeled Transition System (LTS) and the robot mobility is modeled using a Labeled Markov Decision Process (LMDP) with unknown transition probabilities. We use existing product-based model checkers (PMCs) as experts to guide the Q-learning algorithm to convergence. To the best of our knowledge, this is the first approach that models noise in semantic observations using probabilistic labeling functions and employs existing model checkers to provide suboptimal instructions to the Q-learning agent.
Date of Conference: 31 May 2020 - 31 August 2020
Date Added to IEEE Xplore: 15 September 2020
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Conference Location: Paris, France

References

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