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A Framework for Following Temporal Logic Instructions with Unknown Causal Dependencies
[article]
2022
arXiv
pre-print
Teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments is a challenging problem. We consider that user defines every task by a linear temporal logic (LTL) formula. However, some causal dependencies in complex environments may be unknown to the user in advance. Hence, when human user is specifying instructions, the robot cannot solve the tasks by simply following the given instructions. In this work, we propose a hierarchical reinforcement learning
arXiv:2204.03196v3
fatcat:7ghjnp6lznaofpnyietqe2nh5a