3 papers
cs.LG2026
Reward Structure Shapes the Interaction Between Episodic Exploration and Neural Memory in Reinforcement Learning
Jai Malegaonkar, Rohan Patil, Henrik I. Christensen
In partially observable reinforcement learning, agents face a dual bottleneck: they must explore to encounter rewarding states and retain that experience in memory to optimize thei…
cs.AI2026
Randomness is sometimes necessary for coordination
Rohan Patil, Jai Malegaonkar, Henrik I. Christensen
Full parameter sharing is standard in cooperative multi-agent reinforcement learning (MARL) for homogeneous agents. Under permutation-symmetric observations, however, a shared dete…
cs.AI2026
GAMMS: Graph based Adversarial Multiagent Modeling Simulator
Rohan Patil, Jai Malegaonkar, Xiao Jiang +3
As intelligent systems and multi-agent coordination become increasingly central to real-world applications, there is a growing need for simulation tools that are both scalable and…