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cs.LG2026
Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games
Kevin Wang, Kevin Yang, Arjun Prakash +1
We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games. We make three contributions: First, we introdu…
cs.LG2026
Spectral Collapse Drives Loss of Plasticity in Deep Continual Learning
Arjun Prakash, Naicheng He, Kaicheng Guo +5
We investigate why deep neural networks suffer from loss of plasticity in continual learning, and thus fail to learn new tasks without reinitializing parameters. We show that this…
cs.LG2026
Bi-Level Policy Optimization with Nyström Hypergradients
Arjun Prakash, Naicheng He, Denizalp Goktas +2
The dependency of the actor on the critic in actor-critic (AC) reinforcement learning means that AC can be characterized as a bilevel optimization (BLO) problem, also called a Stac…