Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning
The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …
Fault-tolerant predictive control with deep-reinforcement-learning-based torque distribution for four in-wheel motor drive electric vehicles
This article proposes a fault-tolerant control (FTC) method for four in-wheel motor drive
electric vehicles considering both vehicle stability and motor power consumption. First, a …
electric vehicles considering both vehicle stability and motor power consumption. First, a …
Safe deep reinforcement learning for building energy management
The optimization of building energy systems poses a complex challenge due to the dynamic
nature of building environments and the need for ensuring both energy efficiency and …
nature of building environments and the need for ensuring both energy efficiency and …
Multi-objective acoustic sensor placement optimization for crack detection of compressor blade based on reinforcement learning
Nowadays, acoustic sensors have been widely applied for structural health monitoring and
crack detection of compressor blades. As the detection accuracy is mainly affected by signal …
crack detection of compressor blades. As the detection accuracy is mainly affected by signal …
The development of llms for embodied navigation
In recent years, the rapid advancement of Large Language Models (LLMs) such as the
Generative Pre-trained Transformer (GPT) has attracted increasing attention due to their …
Generative Pre-trained Transformer (GPT) has attracted increasing attention due to their …
Expected-mean gamma-incremental reinforcement learning algorithm for robot path planning
CS Tan, R Mohd-Mokhtar, MR Arshad - Expert Systems with Applications, 2024 - Elsevier
Recently, researchers have been extensively exploring the immense potential of Q-Star.
However, the available resources lack comprehensive information on this topic. Despite this …
However, the available resources lack comprehensive information on this topic. Despite this …
Efficient hierarchical reinforcement learning for mapless navigation with predictive neighbouring space scoring
Solving reinforcement learning (RL)-based mapless navigation tasks is challenging due to
their sparse reward and long decision horizon nature. Hierarchical reinforcement learning …
their sparse reward and long decision horizon nature. Hierarchical reinforcement learning …
Advancements and Challenges in Mobile Robot Navigation: A Comprehensive Review of Algorithms and Potential for Self-Learning Approaches
S Al Mahmud, A Kamarulariffin, AM Ibrahim… - Journal of Intelligent & …, 2024 - Springer
Mobile robot navigation has been a very popular topic of practice among researchers since
a while. With the goal of enhancing the autonomy in mobile robot navigation, numerous …
a while. With the goal of enhancing the autonomy in mobile robot navigation, numerous …
Extracting decision tree from trained deep reinforcement learning in traffic signal control
Deep reinforcement learning (DRL) has achieved impressive success in traffic signal control
systems (TSCS). However, since a key component of many DRL models is the complex …
systems (TSCS). However, since a key component of many DRL models is the complex …