A low-rank matrix estimation method in a reward-free RL framework learns shared representations across linear MDPs and yields near-optimal policies with characterized regret bounds under relaxed feature assumptions.
Distral: Robust multitask reinforcement learning
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A single reinforcement learning policy jointly trains multiple locomotion skills for wheeled-legged robots with DC-motor constraints and learns a proprioceptive skill selector for adaptive behavior.
citing papers explorer
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Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
A low-rank matrix estimation method in a reward-free RL framework learns shared representations across linear MDPs and yields near-optimal policies with characterized regret bounds under relaxed feature assumptions.
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MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots
A single reinforcement learning policy jointly trains multiple locomotion skills for wheeled-legged robots with DC-motor constraints and learns a proprioceptive skill selector for adaptive behavior.