Abstract. Reinforcement learning (RL) algorithms combined with modern function approximators such as kernel functions and deep neural networks have achieved ...
Jul 11, 2019 · This question persists even in a basic setting with linear dynamics and linear rewards, for which only linear function approximation is needed.
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael Jordan.
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Oct 19, 2021 · To the best of our knowledge, we establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators.
To the best of our knowledge, we establish the first provably ef- ficient reward-free RL algorithm with kernel and neural function approximators. 1.
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Dec 6, 2020 · Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes ...
To the best of our knowledge, we establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators. Cite this ...
[PDF] On Function Approximation in Reinforcement Learning: Optimism ...
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To the best of our knowledge, this is the first provably efficient framework for reinforcement learning with kernel and neural network function approximations.
To the best of our knowledge, we establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators. ResearchGate ...
This work establishes the first provably efficient reward-free RL algorithm with kernel and neural function approximators, and designs exploration and ...