Proxemic behavior in navigation tasks using reinforcement learning
Human interaction starts with a person approaching another one, respecting their personal
space to prevent uncomfortable feelings. Spatial behavior, called proxemics, allows defining …
space to prevent uncomfortable feelings. Spatial behavior, called proxemics, allows defining …
A conceptual framework for externally-influenced agents: An assisted reinforcement learning review
A long-term goal of reinforcement learning agents is to be able to perform tasks in complex
real-world scenarios. The use of external information is one way of scaling agents to more …
real-world scenarios. The use of external information is one way of scaling agents to more …
Contrastive learning methods for deep reinforcement learning
Deep reinforcement learning (DRL) has shown promising performance in various
application areas (eg, games and autonomous vehicles). Experience replay buffer strategy …
application areas (eg, games and autonomous vehicles). Experience replay buffer strategy …
Human engagement providing evaluative and informative advice for interactive reinforcement learning
Interactive reinforcement learning proposes the use of externally sourced information in
order to speed up the learning process. When interacting with a learner agent, humans may …
order to speed up the learning process. When interacting with a learner agent, humans may …
Accelerating nonlinear dc circuit simulation with reinforcement learning
DC analysis is the foundation for nonlinear electronic circuit simulation. Pseudo transient
analysis (PTA) methods have gained great success among various continuation algorithms …
analysis (PTA) methods have gained great success among various continuation algorithms …
Affordance-based human–robot interaction with reinforcement learning
F Munguia-Galeano, S Veeramani… - IEEE …, 2023 - ieeexplore.ieee.org
Planning precise manipulation in robotics to perform grasp and release-related operations,
while interacting with humans is a challenging problem. Reinforcement learning (RL) has …
while interacting with humans is a challenging problem. Reinforcement learning (RL) has …
Evaluating human-like explanations for robot actions in reinforcement learning scenarios
Explainable artificial intelligence is a research field that tries to provide more transparency
for autonomous intelligent systems. Explainability has been used, particularly in …
for autonomous intelligent systems. Explainability has been used, particularly in …
Reinforcement learning for uav control with policy and reward shaping
C Millán-Arias, R Contreras, F Cruz… - … Conference of the …, 2022 - ieeexplore.ieee.org
In recent years, unmanned aerial vehicle (UAV) related technology has expanded
knowledge in the area, bringing to light new problems and challenges that require solutions …
knowledge in the area, bringing to light new problems and challenges that require solutions …
A state-compensated deep deterministic policy gradient algorithm for UAV trajectory tracking
J Wu, Z Yang, L Liao, N He, Z Wang, C Wang - Machines, 2022 - mdpi.com
The unmanned aerial vehicle (UAV) trajectory tracking control algorithm based on deep
reinforcement learning is generally inefficient for training in an unknown environment, and …
reinforcement learning is generally inefficient for training in an unknown environment, and …
Design of Cognitive Jamming Decision-Making System Against MFR Based on Reinforcement Learning
W Zhang, D Ma, Z Zhao, F Liu - IEEE Transactions on Vehicular …, 2023 - ieeexplore.ieee.org
Electronic countermeasures are developing towards intelligence. The multifunctional radar
changes its working state in real time according to the task requirements. The traditional …
changes its working state in real time according to the task requirements. The traditional …