Safe deep reinforcement learning

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Voltage Control Based on Multi-Agent Safe Deep Reinforcement Learning

Zhongguo dianli
To address issues of voltage limit violations and fluctuations caused by the high penetration of distributed photovoltaic (PV) systems in the distribution network, a voltage control method based on multi-agent safe deep reinforcement learning is proposed.
Yi ZENG +5 more
doaj +1 more source

Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents

Advances in Neural Information Processing Systems 36, 2023
NeurIPS 2023 camera ...
Woojun Kim +3 more
openaire +3 more sources

Understanding representation learning for deep reinforcement learning [PDF]

Representation learning is essential to practical success of reinforcement learning. Through a state representation, an agent can describe its environment to efficiently explore the state space, generalize to new states and perform credit assignment from
Le Lan, Charline
core +1 more source

Safe and Efficient Operation with Constrained Hierarchical Reinforcement Learning

, 2023
Hierarchical Reinforcement Learning (HRL) holds the promise of enhancing sample efficiency and generalization capabilities of Reinforcement Learning (RL) agents by leveraging task decomposition and temporal abstraction, which aligns with human reasoning.
Günnemann, Stephan +2 more
core +1 more source

Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

Tsinghua Science and Technology
In the contemporary era, driverless vehicles are a reality due to the proliferation of distributed technologies, sensing technologies, and Machine to Machine (M2M) communications.
Gajula Ramesh +4 more
doaj +1 more source

A Novel Approach for Train Tracking in Virtual Coupling Based on Soft Actor-Critic

Actuators, 2023
The development of virtual coupling technology provides solutions to the challenges faced by urban rail transit systems. Train tracking control is a crucial component in the operation of virtual coupling, which plays a pivotal role in ensuring the safe ...
Bin Chen +4 more
doaj +1 more source

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

FEBS Letters, EarlyView.
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley +1 more source

Optimizing hybrid electric vehicle coupling organic Rankine cycle energy management strategy via deep reinforcement learning

Energy and AI
Trucks consume a lot of energy. Hybrid technology maintains a long range while realizing energy savings. Hybrid is therefore an effective energy-saving technology for trucks.
Xuanang Zhang +4 more
doaj +1 more source

Safe Adaptive Deep Reinforcement Learning for Autonomous Driving in Urban Environments. Additional Filter? How and Where?

IEEE Access, 2021
Autonomous driving (AD) provides a reliable solution for safe driving by replacing human drivers responsible for the majority of accidents. The emergence of Machine Learning, specifically Deep Reinforcement Learning (DRL), and its ability to solve ...
Sina Alighanbari, Nasser L. Azad
doaj +1 more source

De novo drug design using reinforcement learning with graph-based deep generative models [PDF]

, 2021
Machine learning methods have proven to be effective tools for molecular design, allowing for efficient exploration of the vast chemical space via deep molecular generative models.
Sara, Romeo Atance +4 more
core +1 more source

deep reinforcement learning
reinforcement learning
autonomous driving

path planning
adaptive cruise control
safe reinforcement learning

constrained markov decision process