Recent advances in reinforcement learning in finance
The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …
revolutionized the techniques on data processing and data analysis and brought new …
Reinforcement learning for predictive maintenance: A systematic technical review
The manufacturing world is subject to ever-increasing cost optimization pressures.
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …
Recovery rl: Safe reinforcement learning with learned recovery zones
Safety remains a central obstacle preventing widespread use of RL in the real world:
learning new tasks in uncertain environments requires extensive exploration, but safety …
learning new tasks in uncertain environments requires extensive exploration, but safety …
Safe reinforcement learning with model uncertainty estimates
Many current autonomous systems are being designed with a strong reliance on black box
predictions from deep neural networks (DNNs). However, DNNs tend to be overconfident in …
predictions from deep neural networks (DNNs). However, DNNs tend to be overconfident in …
Exponential bellman equation and improved regret bounds for risk-sensitive reinforcement learning
We study risk-sensitive reinforcement learning (RL) based on the entropic risk measure.
Although existing works have established non-asymptotic regret guarantees for this …
Although existing works have established non-asymptotic regret guarantees for this …
Addressing optimism bias in sequence modeling for reinforcement learning
Impressive results in natural language processing (NLP) based on the Transformer neural
network architecture have inspired researchers to explore viewing offline reinforcement …
network architecture have inspired researchers to explore viewing offline reinforcement …
Dsac: Distributional soft actor critic for risk-sensitive reinforcement learning
In this paper, we present a new reinforcement learning (RL) algorithm called Distributional
Soft Actor Critic (DSAC), which exploits the distributional information of accumulated …
Soft Actor Critic (DSAC), which exploits the distributional information of accumulated …
Risk-sensitive reinforcement learning with function approximation: A debiasing approach
We study function approximation for episodic reinforcement learning with entropic risk
measure. We first propose an algorithm with linear function approximation. Compared to …
measure. We first propose an algorithm with linear function approximation. Compared to …
Risk-sensitive reinforcement learning: Near-optimal risk-sample tradeoff in regret
We study risk-sensitive reinforcement learning in episodic Markov decision processes with
unknown transition kernels, where the goal is to optimize the total reward under the risk …
unknown transition kernels, where the goal is to optimize the total reward under the risk …
Safe exploration algorithms for reinforcement learning controllers
T Mannucci, EJ van Kampen… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
Self-learning approaches, such as reinforcement learning, offer new possibilities for
autonomous control of uncertain or time-varying systems. However, exploring an unknown …
autonomous control of uncertain or time-varying systems. However, exploring an unknown …