9 citations · 19 across the 9 of their papers we have counts for
6 papers · 1 filter
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
Kolby Nottingham, Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra +3
Large language models (LLMs) have recently been used for sequential decision making in interactive environments. However, leveraging environment reward signals for continual LLM ac…
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim +4
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and pla…
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr +4
Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world. However, if initialized with knowledge of high-level subgoals and transitions…
Learning to Query Internet Text for Informing Reinforcement Learning Agents
Kolby Nottingham, Alekhya Pyla, Sameer Singh +1
Generalization to out of distribution tasks in reinforcement learning is a challenging problem. One successful approach improves generalization by conditioning policies on task or…
Guiding Global Placement With Reinforcement Learning
Robert Kirby, Kolby Nottingham, Rajarshi Roy +2
Recent advances in GPU accelerated global and detail placement have reduced the time to solution by an order of magnitude. This advancement allows us to leverage data driven optimi…
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning
Kolby Nottingham, Anand Balakrishnan, Jyotirmoy Deshmukh +1
It is notoriously difficult to control the behavior of reinforcement learning agents. Agents often learn to exploit the environment or reward signal and need to be retrained multip…