2 papers
cs.LG2025
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…
cs.LG2025
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…