Deep reinforcement learning: a survey
Deep reinforcement learning (RL) has become one of the most popular topics in artificial
intelligence research. It has been widely used in various fields, such as end-to-end control …
intelligence research. It has been widely used in various fields, such as end-to-end control …
Goal-conditioned imitation learning
Y Ding, C Florensa, P Abbeel… - Advances in neural …, 2019 - proceedings.neurips.cc
Abstract Designing rewards for Reinforcement Learning (RL) is challenging because it
needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter …
needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter …
Predicting gait adaptations due to ankle plantarflexor muscle weakness and contracture using physics-based musculoskeletal simulations
Deficits in the ankle plantarflexor muscles, such as weakness and contracture, occur
commonly in conditions such as cerebral palsy, stroke, muscular dystrophy, Charcot-Marie …
commonly in conditions such as cerebral palsy, stroke, muscular dystrophy, Charcot-Marie …
Physics-informed deep reinforcement learning-based integrated two-dimensional car-following control strategy for connected automated vehicles
Connected automated vehicles (CAVs) are broadly recognized as next-generation
transformative transportation technologies having great potential to improve traffic safety …
transformative transportation technologies having great potential to improve traffic safety …
Sub-policy adaptation for hierarchical reinforcement learning
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-
making problems with sparse rewards. Unfortunately, most methods still decouple the lower …
making problems with sparse rewards. Unfortunately, most methods still decouple the lower …
[PDF][PDF] Is multiagent deep reinforcement learning the answer or the question? A brief survey
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …
led to a dramatic increase in the number of applications and methods. Recent works have …
Position control of an acoustic cavitation bubble by reinforcement learning
K Klapcsik, B Gyires-Tóth, JM Rosselló… - arXiv preprint arXiv …, 2023 - arxiv.org
A control technique is developed via Reinforcement Learning that allows arbitrary
controlling of the position of an acoustic cavitation bubble in a dual-frequency standing …
controlling of the position of an acoustic cavitation bubble in a dual-frequency standing …
LORM: a novel reinforcement learning framework for biped gait control
W Zhang, Y Jiang, FUD Farrukh, C Zhang… - PeerJ Computer …, 2022 - peerj.com
Legged robots are better able to adapt to different terrains compared with wheeled robots.
However, traditional motion controllers suffer from extremely complex dynamics properties …
However, traditional motion controllers suffer from extremely complex dynamics properties …
Active exploration deep reinforcement learning for continuous action space with forward prediction
D Zhao, X Huanshi, Z Xun - International Journal of Computational …, 2024 - Springer
The application of reinforcement learning (RL) to the field of autonomous robotics has high
requirements about sample efficiency, since the agent expends for interaction with the …
requirements about sample efficiency, since the agent expends for interaction with the …
Combining Physics and Deep Learning for Continuous-Time Dynamics Models
M Lutter - Inductive Biases in Machine Learning for Robotics and …, 2023 - Springer
During the last five years, deep learning has shown the potential to fundamentally change
the use of learning in robotics. Currently, many robot learning approaches involve a deep …
the use of learning in robotics. Currently, many robot learning approaches involve a deep …