3 citations · 8 across the 4 of their papers we have counts for
5 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…
Be Considerate: Objectives, Side Effects, and Deciding How to Act
Parand Alizadeh Alamdari, Toryn Q. Klassen, Rodrigo Toro Icarte +1
Recent work in AI safety has highlighted that in sequential decision making, objectives are often underspecified or incomplete. This gives discretion to the acting agent to realize…
The act of remembering: a study in partially observable reinforcement learning
Rodrigo Toro Icarte, Richard Valenzano, Toryn Q. Klassen +3
Reinforcement Learning (RL) agents typically learn memoryless policies---policies that only consider the last observation when selecting actions. Learning memoryless policies is ef…
Towards the Role of Theory of Mind in Explanation
Maayan Shvo, Toryn Q. Klassen, Sheila A. McIlraith
Theory of Mind is commonly defined as the ability to attribute mental states (e.g., beliefs, goals) to oneself, and to others. A large body of previous work - from the social scien…