6 papers
Zero Shot Coordination for Sparse Reward Tasks with Diverse Reward Shapings
Keenan Powell, Peihong Yu, Pratap Tokekar
Many Multi-Agent Reinforcement Learning (MARL) agents fail to adapt properly to cooperating with agents trained with the same objectives but different seeds, algorithms, or other t…
Distributionally Robust Self Paced Curriculum Reinforcement Learning
Anirudh Satheesh, Keenan Powell, Vaneet Aggarwal
A central challenge in reinforcement learning is that policies trained in controlled environments often fail under distribution shifts at deployment into real-world environments. D…
Primal-Only Actor Critic Algorithm for Robust Constrained Average Cost MDPs
Anirudh Satheesh, Sooraj Sathish, Swetha Ganesh +2
In this work, we study the problem of finding robust and safe policies in Robust Constrained Average-Cost Markov Decision Processes (RCMDPs). A key challenge in this setting is the…
cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending
Anirudh Satheesh, Keenan Powell, Hua Wei
Many multi-agent reinforcement learning (MARL) algorithms are trained in fixed simulation environments, making them brittle when deployed in real-world scenarios with more complex…
MORSE-500: A Programmatically Controllable Video Benchmark to Stress-Test Multimodal Reasoning
Zikui Cai, Andrew Wang, Anirudh Satheesh +10
Despite rapid advances in vision-language models (VLMs), current benchmarks for multimodal reasoning fall short in three key dimensions. First, they overwhelmingly rely on static i…
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
Anirudh Satheesh, Keenan Powell
Traffic congestion in modern cities is exacerbated by the limitations of traditional fixed-time traffic signal systems, which fail to adapt to dynamic traffic patterns. Adaptive Tr…