Ms.PR applies multi-scale predictive supervision to enforce goal-directed alignment in latent spaces for offline GCRL, yielding improved representation quality and performance on vision and state-based tasks.
A survey of state representation learning for deep reinforcement learning.arXiv preprint arXiv:2506.17518
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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.
citing papers explorer
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Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning
Ms.PR applies multi-scale predictive supervision to enforce goal-directed alignment in latent spaces for offline GCRL, yielding improved representation quality and performance on vision and state-based tasks.
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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.