Results 11 to 20 of about 6,944,236 (307)

Avalanche RL: A Continual Reinforcement Learning Library

, 2022
Presented at the 21st International Conference on Image Analysis and Processing (ICIAP 2021)
Lucchesi N., Carta A., Lomonaco V., Bacciu D.   +3 more
openaire   +3 more sources

An Integrated Artificial Intelligence Framework for Nanomedicine Engineering. [PDF]

Adv Sci (Weinh)
This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Dong M, Luo Q, Ding X, Lu C, Zhang R, Tan Q, Pang Z, Ouyang D, Li D, Xia J, Wang J.   +10 more
europepmc   +2 more sources

RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization [PDF]

Advances in Neural Information Processing Systems 36, 2023
Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization. Despite the focus on algorithms aimed at resolving visual generalization problems, we argue that the devil is in the existing benchmarks as they are restricted to isolated ...
Zhecheng Yuan, Sizhe Yang, Pu Hua, Can Chang, Kaizhe Hu, Xiaolong Wang, Huazhe Xu   +6 more
core   +5 more sources

RL-I2IT: Image-to-image translation with deep reinforcement learning [PDF]

Neural Networks
Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model. However, designing such a single-step model is always challenging, requiring a huge number of parameters and easily falling into bad global minimums and overfitting.
Jing Hu, Ziwei Luo, Chengming Feng, Shu Hu, Bin Zhu, Xi Wu, Xin Li, Hongtu Zhu, Siwei Lyu, Xin Wang   +9 more
openaire   +5 more sources

Performance characterization of reinforcement learning-enabled evolutionary algorithms for integrated school bus routing and scheduling problem

International Journal of Cognitive Computing in Engineering, 2021
Bi-objective school bus scheduling optimization problem that is a subset of vehicle fleet scheduling problem is focused in this paper. In the literature, school bus routing and scheduling problem is proven to be an NP-Hard problem.
Eda Koksal, Abhishek R. Hegde, Haresh P. Pandiarajan, Bharadwaj Veeravalli   +3 more
doaj   +1 more source

Dueling RL: Reinforcement Learning with Trajectory Preferences

CoRR, 2021
Aadirupa Saha and Aldo Pacchiano contributed ...
Aadirupa Saha, Aldo Pacchiano, Jonathan Lee 0002   +2 more
openaire   +3 more sources

CFR-RL: Traffic Engineering With Reinforcement Learning in SDN [PDF]

IEEE Journal on Selected Areas in Communications, 2020
Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network.
Junjie Zhang 0001, Minghao Ye, Zehua Guo 0001, Chen-Yu Yen, H. Jonathan Chao   +4 more
openaire   +4 more sources

RL-IoT: Reinforcement Learning to Interact with IoT Devices [PDF]

2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 2021
Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is the key for interoperability and automatic verification of their capabilities.
Giulia Milan, Luca Vassio, Idilio Drago, Marco Mellia   +3 more
openaire   +3 more sources

Robust Reinforcement Learning: A Review of Foundations and Recent Advances

Machine Learning and Knowledge Extraction, 2022
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 environments, solution robustness becomes an increasingly important aspect of RL ...
Janosch Moos, Kay Hansel, Hany Abdulsamad, Svenja Stark, Debora Clever, Jan Peters   +5 more
doaj   +1 more source

Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Mendel, 2021
Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots.
Roman Parak, Radomil Matousek
doaj   +1 more source