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Communications biology
Dec 16, 2021
Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. Howeve...
Computational intelligence and neuroscience
Dec 16, 2021
Developing artificial intelligence (AI) agents is challenging for efficient exploration in visually rich and complex environments. In this study, we formulate the exploration question as a reinforcement learning problem and rely on intrinsic motivati...
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Dec 13, 2021
In this paper, we present an implementation of social learning for swarm robotics. We consider social learning as a distributed online reinforcement learning method applied to a collective of robots where sensing, acting and coordination are performe...
IEEE transactions on neural networks and learning systems
Nov 30, 2021
The current reward learning from human preferences could be used to resolve complex reinforcement learning (RL) tasks without access to a reward function by defining a single fixed preference between pairs of trajectory segments. However, the judgmen...
Sensors (Basel, Switzerland)
Nov 25, 2021
Reinforcement learning (RL) is a booming area in artificial intelligence. The applications of RL are endless nowadays, ranging from fields such as medicine or finance to manufacturing or the gaming industry. Although multiple works argue that RL can ...
IEEE transactions on neural networks and learning systems
Oct 27, 2021
In this article, we discuss H control for unknown nonlinear systems in discrete time. A discrete-time recurrent neural network is used to model the nonlinear system, and then, the H tracking control is applied based on the neural model. Since this ne...
Neural networks : the official journal of the International Neural Network Society
Sep 17, 2021
Spiking neural networks (SNNs) aim to replicate energy efficiency, learning speed and temporal processing of biological brains. However, accuracy and learning speed of such networks is still behind reinforcement learning (RL) models based on traditio...
Sensors (Basel, Switzerland)
Sep 2, 2021
External disturbance poses the primary threat to robot balance in dynamic environments. This paper provides a learning-based control architecture for quadrupedal self-balancing, which is adaptable to multiple unpredictable scenes of external continuo...
Topics in cognitive science
Sep 1, 2021
We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provi...
Trends in neurosciences
Sep 1, 2021
Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial i...