1 citations · 3 across the 5 of their papers we have counts for
5 papers
A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study
Jingyi Liu, Jian Guo, Eberhard Gill
Reinforcement learning algorithms have been widely used in dynamic and control systems. However, interpreting their internal learning behavior remains a challenge. In the authors'…
Adapting Critic Match Loss Landscape Visualization to Off-policy Reinforcement Learning
Jingyi Liu, Jian Guo, Eberhard Gill
This work extends an established critic match loss landscape visualization method from online to off-policy reinforcement learning (RL), aiming to reveal the optimization geometry…
Visualizing Critic Match Loss Landscapes for Interpretation of Online Reinforcement Learning Control Algorithms
Jingyi Liu, Jian Guo, Eberhard Gill
Reinforcement learning has proven its power on various occasions. However, its performance is not always guaranteed when system dynamics change. Instead, it largely relies on users…
A model-based framework for learning transparent swarm behaviors
Mario Coppola, Jian Guo, Eberhard Gill +1
This paper proposes a model-based framework to automatically and efficiently design understandable and verifiable behaviors for swarms of robots. The framework is based on the auto…
Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning
Mario Coppola, Jian Guo, Eberhard K. A. Gill +1
In this work, we explore emergent behaviors by swarms of anonymous, homogeneous, non-communicating, reactive robots that do not know their global position and have limited relative…