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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.LG2026
BOIL: Learning Environment Personalized Information
Rohan Patil, Henrik I. Christensen
Navigating complex environments poses challenges for multi-agent systems, requiring efficient extraction of insights from limited information. In this paper, we introduce the Black…
cs.LG2025
Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation
Abdulaziz Almuzairee, Rohan Patil, Dwait Bhatt +1
Vision is well-known for its use in manipulation, especially using visual servoing. Due to the 3D nature of the world, using multiple camera views and merging them creates better r…