5 papers
Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling
Marc Walden, Jason Liu, Shaashwath Sivakumar +2
We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. First, we introduce a…
Efficiently Solving Mixed-Hierarchy Games with Quasi-Policy Approximations
Hamzah Khan, Dong Ho Lee, Jingqi Li +5
Multi-robot coordination often exhibits hierarchical structure, with some robots' decisions depending on the planned behaviors of others. While game theory provides a principled fr…
What Do Agents Think One Another Want? Level-2 Inverse Games for Inferring Agents' Estimates of Others' Objectives
Hamzah I. Khan, Jingqi Li, David Fridovich-Keil
Effectively interpreting strategic interactions among multiple agents requires us to infer each agent's objective from limited information. Existing inverse game-theoretic approach…
Act Natural! Extending Naturalistic Projection to Multimodal Behavior Scenarios
Hamzah I. Khan, David Fridovich-Keil
Autonomous agents operating in public spaces must consider how their behaviors might affect the humans around them, even when not directly interacting with them. To this end, it is…
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games
Shubhankar Agarwal, Hamzah I. Khan, Sandeep P. Chinchali +1
Saddle point optimization is a critical problem employed in numerous real-world applications, including portfolio optimization, generative adversarial networks, and robotics. It ha…