21 citations · 52 across the 15 of their papers we have counts for
3 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…
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning
Jonathan C. Balloch, Rishav Bhagat, Geigh Zollicoffer +3
In deep reinforcement learning (RL) research, there has been a concerted effort to design more efficient and productive exploration methods while solving sparse-reward problems. Th…