19 citations · 27 across the 6 of their papers we have counts for
7 papers
Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior
Phillip J. K. Christoffersen, Andrew C. Li, Rodrigo Toro Icarte +1
Many real-world reinforcement learning (RL) problems necessitate learning complex, temporally extended behavior that may only receive reward signal when the behavior is completed.…
Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines
Andrew C. Li, Zizhao Chen, Pashootan Vaezipoor +3
Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions. We investigate how to generate policies via RL when reward func…
Learning to Follow Instructions in Text-Based Games
Mathieu Tuli, Andrew C. Li, Pashootan Vaezipoor +3
Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observatio…
Challenges to Solving Combinatorially Hard Long-Horizon Deep RL Tasks
Andrew C. Li, Pashootan Vaezipoor, Rodrigo Toro Icarte +1
Deep reinforcement learning has shown promise in discrete domains requiring complex reasoning, including games such as Chess, Go, and Hanabi. However, this type of reasoning is les…
LTL2Action: Generalizing LTL Instructions for Multi-Task RL
Pashootan Vaezipoor, Andrew Li, Rodrigo Toro Icarte +1
We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. Instructions are expressed in a well-known formal lan…
Interpretable Sequence Classification via Discrete Optimization
Maayan Shvo, Andrew C. Li, Rodrigo Toro Icarte +1
Sequence classification is the task of predicting a class label given a sequence of observations. In many applications such as healthcare monitoring or intrusion detection, early c…
RNA-RNA interaction prediction based on multiple sequence alignments
Andrew X. Li, Manja Marz, Jing Qin +1
Many computerized methods for RNA-RNA interaction structure prediction have been developed. Recently, time and space dynamic programming algorithms have become av…