3 citations · 5 across the 2 of their papers we have counts for
3 papers
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…
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…