Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Robust reinforcement learning: A review of foundations and recent advances
J Moos, K Hansel, H Abdulsamad, S Stark… - Machine Learning and …, 2022 - mdpi.com
Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Tilted empirical risk minimization
Empirical risk minimization (ERM) is typically designed to perform well on the average loss,
which can result in estimators that are sensitive to outliers, generalize poorly, or treat …
which can result in estimators that are sensitive to outliers, generalize poorly, or treat …
Conditionally elicitable dynamic risk measures for deep reinforcement learning
A Coache, S Jaimungal, Á Cartea - SIAM Journal on Financial Mathematics, 2023 - SIAM
We propose a novel framework to solve risk-sensitive reinforcement learning problems
where the agent optimizes time-consistent dynamic spectral risk measures. Based on the …
where the agent optimizes time-consistent dynamic spectral risk measures. Based on the …
Reinforcement learning with dynamic convex risk measures
A Coache, S Jaimungal - Mathematical Finance, 2024 - Wiley Online Library
We develop an approach for solving time‐consistent risk‐sensitive stochastic optimization
problems using model‐free reinforcement learning (RL). Specifically, we assume agents …
problems using model‐free reinforcement learning (RL). Specifically, we assume agents …
Bridging distributional and risk-sensitive reinforcement learning with provable regret bounds
We study the regret guarantee for risk-sensitive reinforcement learning (RSRL) via
distributional reinforcement learning (DRL) methods. In particular, we consider finite …
distributional reinforcement learning (DRL) methods. In particular, we consider finite …
Risk-sensitive reinforcement learning with exponential criteria
E Noorani, C Mavridis, J Baras - arxiv preprint arxiv:2212.09010, 2022 - arxiv.org
While reinforcement learning has shown experimental success in a number of applications,
it is known to be sensitive to noise and perturbations in the parameters of the system …
it is known to be sensitive to noise and perturbations in the parameters of the system …
[PDF][PDF] Risk-aware transfer in reinforcement learning using successor features
M Gimelfarb, A Barreto, S Sanner… - Advances in Neural …, 2021 - proceedings.neurips.cc
Sample efficiency and risk-awareness are central to the development of practical
reinforcement learning (RL) for complex decision-making. The former can be addressed by …
reinforcement learning (RL) for complex decision-making. The former can be addressed by …
Cascaded gaps: Towards logarithmic regret for risk-sensitive reinforcement learning
In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement
learning based on the entropic risk measure. We propose a novel definition of sub-optimality …
learning based on the entropic risk measure. We propose a novel definition of sub-optimality …
Policy gradient bayesian robust optimization for imitation learning
The difficulty in specifying rewards for many real-world problems has led to an increased
focus on learning rewards from human feedback, such as demonstrations. However, there …
focus on learning rewards from human feedback, such as demonstrations. However, there …
Risk-sensitive reinforce: A monte carlo policy gradient algorithm for exponential performance criteria
Risk is an inherent component of any decision making process under uncertain conditions,
and failure to consider risk may lead to significant performance degradation. We present a …
and failure to consider risk may lead to significant performance degradation. We present a …