A federated actor-critic framework lets agents share a linear subspace representation for policies while maintaining personalized local actors and critics, achieving critic error and policy gradient convergence rates of order 1 over square root of TK with linear speedup in K agents under environment
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Harder classification tasks produce neural representations whose accuracy collapses under binarization and shuffling while easier tasks remain robust, defining task complexity via the performance gap between full-precision and perturbed networks.
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Collaborative Yet Personalized Policy Training: Single-Timescale Federated Actor-Critic
A federated actor-critic framework lets agents share a linear subspace representation for policies while maintaining personalized local actors and critics, achieving critic error and policy gradient convergence rates of order 1 over square root of TK with linear speedup in K agents under environment
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Task complexity shapes internal representations and robustness in neural networks
Harder classification tasks produce neural representations whose accuracy collapses under binarization and shuffling while easier tasks remain robust, defining task complexity via the performance gap between full-precision and perturbed networks.