21 citations · 52 across the 15 of their papers we have counts for
6 papers · 1 filter
The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited
Kenneth Eaton, Jonathan Balloch, Julia Kim +1
Interpretability of deep reinforcement learning systems could assist operators with understanding how they interact with their environment. Vector quantization methods -- also call…
External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling
Rishav Bhagat, Jonathan Balloch, Zhiyu Lin +2
Unlike reinforcement learning (RL) agents, humans remain capable multitaskers in changing environments. In spite of only experiencing the world through their own observations and i…
Novelty Detection in Reinforcement Learning with World Models
Geigh Zollicoffer, Kenneth Eaton, Jonathan Balloch +4
Reinforcement learning (RL) using world models has found significant recent successes. However, when a sudden change to world mechanics or properties occurs then agent performance…
Neuro-Symbolic World Models for Adapting to Open World Novelty
Jonathan Balloch, Zhiyu Lin, Robert Wright +5
Open-world novelty--a sudden change in the mechanics or properties of an environment--is a common occurrence in the real world. Novelty adaptation is an agent's ability to improve…
NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty
Jonathan Balloch, Zhiyu Lin, Mustafa Hussain +5
A robust body of reinforcement learning techniques have been developed to solve complex sequential decision making problems. However, these methods assume that train and evaluation…
Detecting and Adapting to Novelty in Games
Xiangyu Peng, Jonathan C. Balloch, Mark O. Riedl
Open-world novelty occurs when the rules of an environment can change abruptly, such as when a game player encounters "house rules". To address open-world novelty, game playing age…