188 citations · 199 across the 16 of their papers we have counts for
7 papers · 1 filter
Ground-Compose-Reinforce: Grounding Language in Agentic Behaviours using Limited Data
Andrew C. Li, Toryn Q. Klassen, Andrew Wang +2
Grounding language in perception and action is a key challenge when building situated agents that can interact with humans, or other agents, via language. In the past, addressing t…
Reward Machines for Deep RL in Noisy and Uncertain Environments
Andrew C. Li, Zizhao Chen, Toryn Q. Klassen +3
Reward Machines provide an automaton-inspired structure for specifying instructions, safety constraints, and other temporally extended reward-worthy behaviour. By exposing the unde…
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…
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning
Rodrigo Toro Icarte, Ethan Waldie, Toryn Q. Klassen +3
Reinforcement learning (RL) is a central problem in artificial intelligence. This problem consists of defining artificial agents that can learn optimal behaviour by interacting wit…
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…
Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano +1
Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover reward…