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…
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…
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…
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…