@article {5069, title = {Compositional RL Agents That Follow Language Commands in Temporal Logic}, journal = {Frontiers in Robotics and AI}, volume = {8}, year = {2021}, month = {07/2022}, abstract = {

We demonstrate how a reinforcement learning agent can use compositional recurrent neural networks to learn to carry out commands specified in linear temporal logic (LTL). Our approach takes as input an LTL formula, structures a deep network according to the parse of the formula, and determines satisfying actions. This compositional structure of the network enables zero-shot generalization to significantly more complex unseen formulas. We demonstrate this ability in multiple problem domains with both discrete and continuous state-action spaces. In a symbolic domain, the agent finds a sequence of letters that satisfy a specification. In a Minecraft-like environment, the agent finds a sequence of actions that conform to a formula. In the Fetch environment, the robot finds a sequence of arm configurations that move blocks on a table to fulfill the commands. While most prior work can learn to execute one formula reliably, we develop a novel form of multi-task learning for RL agents that allows them to learn from a diverse set of tasks and generalize to a new set of diverse tasks without any additional training. The compositional structures presented here are not specific to LTL, thus opening the path to RL agents that perform zero-shot generalization in other compositional domains.

}, doi = {10.3389/frobt.2021.689550}, url = {https://www.frontiersin.org/articles/10.3389/frobt.2021.689550/full}, author = {Kuo, Yen-Ling and Katz, Boris and Andrei Barbu} }