Intervention on a fixed-size recurrent state enables contextual control in sequential decisions without memory growth or direct context input.
Deep recurrent q-learning for partially observable mdps.arXiv preprint arXiv:1507.06527
5 Pith papers cite this work. Polarity classification is still indexing.
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ARL lifts states into signature-augmented manifolds and employs self-consistent proxies of future path-laws to enable deterministic expected-return evaluation while preserving contraction mappings in jump-diffusion environments.
ALFWorld aligns text-based and embodied visual environments so agents can learn abstract policies in TextWorld that transfer to better performance on ALFRED tasks than visual-only training.
Belief-state RWKV maintains an uncertainty-aware recurrent state for RL policies in partial observability and shows modest gains over standard recurrent baselines in a pilot with observation noise.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
citing papers explorer
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Contextual Control without Memory Growth in a Context-Switching Task
Intervention on a fixed-size recurrent state enables contextual control in sequential decisions without memory growth or direct context input.
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Anticipatory Reinforcement Learning: From Generative Path-Laws to Distributional Value Functions
ARL lifts states into signature-augmented manifolds and employs self-consistent proxies of future path-laws to enable deterministic expected-return evaluation while preserving contraction mappings in jump-diffusion environments.
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ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
ALFWorld aligns text-based and embodied visual environments so agents can learn abstract policies in TextWorld that transfer to better performance on ALFRED tasks than visual-only training.
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Belief-State RWKV for Reinforcement Learning under Partial Observability
Belief-state RWKV maintains an uncertainty-aware recurrent state for RL policies in partial observability and shows modest gains over standard recurrent baselines in a pilot with observation noise.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.