Showing cs.AIShow all
3 papers · 1 filter
cs.AI2026
Explainable Model Routing for Agentic Workflows
Mika Okamoto, Ansel Kaplan Erol, Mark Riedl
Modern agentic workflows decompose complex tasks into specialized subtasks and route them to diverse models to minimize cost without sacrificing quality. However, current routing a…
cs.AI2024
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…
cs.AI2024
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…