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A review of safe reinforcement learning: Methods, theory and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
A review of safe reinforcement learning: Methods, theories and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
Nearly minimax optimal reinforcement learning for linear mixture markov decision processes
We study reinforcement learning (RL) with linear function approximation where the
underlying transition probability kernel of the Markov decision process (MDP) is a linear …
underlying transition probability kernel of the Markov decision process (MDP) is a linear …
Nearly minimax optimal reinforcement learning for linear markov decision processes
We study reinforcement learning (RL) with linear function approximation. For episodic time-
inhomogeneous linear Markov decision processes (linear MDPs) whose transition …
inhomogeneous linear Markov decision processes (linear MDPs) whose transition …
Settling the sample complexity of model-based offline reinforcement learning
Settling the sample complexity of model-based offline reinforcement learning Page 1 The
Annals of Statistics 2024, Vol. 52, No. 1, 233–260 https://doi.org/10.1214/23-AOS2342 © …
Annals of Statistics 2024, Vol. 52, No. 1, 233–260 https://doi.org/10.1214/23-AOS2342 © …
VOL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation
We study time-inhomogeneous episodic reinforcement learning (RL) under general function
approximation and sparse rewards. We design a new algorithm, Variance-weighted …
approximation and sparse rewards. We design a new algorithm, Variance-weighted …
Breaking the sample complexity barrier to regret-optimal model-free reinforcement learning
Achieving sample efficiency in online episodic reinforcement learning (RL) requires
optimally balancing exploration and exploitation. When it comes to a finite-horizon episodic …
optimally balancing exploration and exploitation. When it comes to a finite-horizon episodic …
Nearly minimax optimal reinforcement learning with linear function approximation
We study reinforcement learning with linear function approximation where the transition
probability and reward functions are linear with respect to a feature map** $\boldsymbol …
probability and reward functions are linear with respect to a feature map** $\boldsymbol …
Made: Exploration via maximizing deviation from explored regions
In online reinforcement learning (RL), efficient exploration remains particularly challenging
in high-dimensional environments with sparse rewards. In low-dimensional environments …
in high-dimensional environments with sparse rewards. In low-dimensional environments …
Learning stochastic shortest path with linear function approximation
We study the stochastic shortest path (SSP) problem in reinforcement learning with linear
function approximation, where the transition kernel is represented as a linear mixture of …
function approximation, where the transition kernel is represented as a linear mixture of …