A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
Breast cancer is one of the most commonly diagnosed cancer types in the woman and
automatically classifying breast cancer histopathological images is an important task in …
automatically classifying breast cancer histopathological images is an important task in …
Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters
Abstract Multispectral LiDAR (Light Detection And Ranging) is characterized of the
completeness and consistency of its spectrum and spatial geometric data, which provides a …
completeness and consistency of its spectrum and spatial geometric data, which provides a …
[HTML][HTML] Solving an energy resource management problem with a novel multi-objective evolutionary reinforcement learning method
GMC Leite, S Jiménez-Fernández… - Knowledge-Based …, 2023 - Elsevier
Microgrids have become popular candidates for integrating diverse energy sources into the
power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is …
power grid as means of reducing fossil fuel usage. Energy Resource Management (ERM) is …
Multi-objective deep reinforcement learning for optimal design of wind turbine blade
Z Wang, T Zeng, X Chu, D Xue - Renewable Energy, 2023 - Elsevier
The design of a wind turbine blade is a typical complex multi-objective optimization problem,
mostly solved by evolutionary algorithms. However, these methods are not effective due to …
mostly solved by evolutionary algorithms. However, these methods are not effective due to …
Multi-objective deep reinforcement learning for personalized dose optimization based on multi-indicator experience replay
L Huo, Y Tang - Applied Sciences, 2022 - mdpi.com
Chemotherapy as an effective method is now widely used to treat various types of malignant
tumors. With advances in medicine and drug dosimetry, the precise dose adjustment of …
tumors. With advances in medicine and drug dosimetry, the precise dose adjustment of …
Mrcdrl: Multi-robot coordination with deep reinforcement learning
D Wang, H Deng, Z Pan - Neurocomputing, 2020 - Elsevier
This paper proposes a multi-robot cooperative algorithm based on deep reinforcement
learning (MRCDRL). We use end-to-end methods to train directly from each robot-centered …
learning (MRCDRL). We use end-to-end methods to train directly from each robot-centered …
Sparse markov decision processes with causal sparse tsallis entropy regularization for reinforcement learning
In this letter, a sparse Markov decision process (MDP) with novel causal sparse Tsallis
entropy regularization is proposed. The proposed policy regularization induces a sparse …
entropy regularization is proposed. The proposed policy regularization induces a sparse …
The impact of environmental stochasticity on value-based multiobjective reinforcement learning
A common approach to address multiobjective problems using reinforcement learning
methods is to extend model-free, value-based algorithms such as Q-learning to use a vector …
methods is to extend model-free, value-based algorithms such as Q-learning to use a vector …
Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety
The concept of impact-minimisation has previously been proposed as an approach to
addressing the safety concerns that can arise from utility-maximising agents. An impact …
addressing the safety concerns that can arise from utility-maximising agents. An impact …