19 citations · 66 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Augment with Care: Contrastive Learning for Combinatorial Problems
Haonan Duan, Pashootan Vaezipoor, Max B. Paulus +2
Supervised learning can improve the design of state-of-the-art solvers for combinatorial problems, but labelling large numbers of combinatorial instances is often impractical due t…
Learning Branching Heuristics for Propositional Model Counting
Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu +4
Propositional model counting, or #SAT, is the problem of computing the number of satisfying assignments of a Boolean formula. Many problems from different application areas, includ…