Reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees
We present a model-free reinforcement learning algorithm to synthesize control policies that maximize the probability of satisfying high-level control objectives given as Linear Temporal Logic (LTL) formulas. Uncertainty is considered in the workspace properties, the structure of the workspace, and...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2020
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