31 citations · 67 across the 10 of their papers we have counts for
6 papers · 1 filter
The Logical Options Framework
Brandon Araki, Xiao Li, Kiran Vodrahalli +3
Learning composable policies for environments with complex rules and tasks is a challenging problem. We introduce a hierarchical reinforcement learning framework called the Logical…
Automata Guided Reinforcement Learning With Demonstrations
Xiao Li, Yao Ma, Calin Belta
Tasks with complex temporal structures and long horizons pose a challenge for reinforcement learning agents due to the difficulty in specifying the tasks in terms of reward functio…
Automata-Guided Hierarchical Reinforcement Learning for Skill Composition
Xiao Li, Yao Ma, Calin Belta
Skills learned through (deep) reinforcement learning often generalizes poorly across domains and re-training is necessary when presented with a new task. We present a framework tha…
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks
Xiao Li, Yao Ma, Calin Belta
Reward engineering is an important aspect of reinforcement learning. Whether or not the user's intentions can be correctly encapsulated in the reward function can significantly imp…
Reinforcement Learning With Temporal Logic Rewards
Xiao Li, Cristian-Ioan Vasile, Calin Belta
Reinforcement learning (RL) depends critically on the choice of reward functions used to capture the de- sired behavior and constraints of a robot. Usually, these are handcrafted b…
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks
Xiao Li, Calin Belta
Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into devel…