TabQL is a reinforcement learning framework that substitutes a tabular foundation model with in-context capabilities for the parametric Q-network in DQN, with a warm-up phase and theoretical analysis claiming improved sample efficiency.
Deep reinforcement learning for robotics: A survey of real-world successes.Annual Review of Control, Robotics, and Autonomous Systems, 8(1):153–188
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CoPhy is a new RL framework that distills VLM cognition into BEV encoders, adds an auto-regressive BEV world model for action-conditioned future prediction, and optimizes policies via GRPO with dual physical-cognitive rewards, claiming SOTA on NAVSIM v1/v2.
Soft-DPG uses Gaussian smoothing on the Bellman equation to derive a well-defined policy gradient without relying on critic action derivatives, yielding competitive performance on dense-reward tasks and gains on discretized-reward variants.
citing papers explorer
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TabQL: In-Context Q-Learning with Tabular Foundation Models
TabQL is a reinforcement learning framework that substitutes a tabular foundation model with in-context capabilities for the parametric Q-network in DQN, with a warm-up phase and theoretical analysis claiming improved sample efficiency.
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Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
CoPhy is a new RL framework that distills VLM cognition into BEV encoders, adds an auto-regressive BEV world model for action-conditioned future prediction, and optimizes policies via GRPO with dual physical-cognitive rewards, claiming SOTA on NAVSIM v1/v2.
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Soft Deterministic Policy Gradient with Gaussian Smoothing
Soft-DPG uses Gaussian smoothing on the Bellman equation to derive a well-defined policy gradient without relying on critic action derivatives, yielding competitive performance on dense-reward tasks and gains on discretized-reward variants.