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 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 © …
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 …
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 …
Learning adversarial low-rank markov decision processes with unknown transition and full-information feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-
information feedback setting. In particular, the unknown transition probability kernel admits a …
information feedback setting. In particular, the unknown transition probability kernel admits a …
On the sample complexity of learning infinite-horizon discounted linear kernel MDPs
We study reinforcement learning for infinite-horizon discounted linear kernel MDPs, where
the transition probability function is linear in a predefined feature mapping. Existing …
the transition probability function is linear in a predefined feature mapping. Existing …
A nearly optimal and low-switching algorithm for reinforcement learning with general function approximation
The exploration-exploitation dilemma has been a central challenge in reinforcement
learning (RL) with complex model classes. In this paper, we propose a new algorithm …
learning (RL) with complex model classes. In this paper, we propose a new algorithm …