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Learning Reward Machines through Preference Queries over Sequences
Reward machines have shown great promise at capturing non-Markovian reward functions for learning tasks that involve complex action sequencing.
Generalizing to New Domains by Mapping Natural Language to Lifted LTL
We compare our method of mapping natural language task specifications to intermediate contextual queries against state-of-the-art CopyNet models capable of translating natural language to LTL, by evaluating whether correct LTL for manipulation and navigation task specifications can be output, and show that our method outperforms the CopyNet model on unseen object references.
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estimates.
