Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents

1 code implementation • 6 Feb 2024 • Yash Shukla, Tanushree Burman, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov

In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions.

Continuous Control Decision Making +3