Neural networks : the official journal of the International Neural Network Society
Mar 23, 2022
Recent theoretical and experimental works have connected Hebbian plasticity with the reinforcement learning (RL) paradigm, producing a class of trial-and-error learning in artificial neural networks known as neo-Hebbian plasticity. Inspired by the ro...
Training agents via deep reinforcement learning with sparse rewards for robotic control tasks in vast state space are a big challenge, due to the rareness of successful experience. To solve this problem, recent breakthrough methods, the hindsight exp...
Neural networks : the official journal of the International Neural Network Society
Mar 10, 2022
In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that should drive ...
Computational intelligence and neuroscience
Mar 4, 2022
In the real world, there are a variety of situations that require strategy control, that is reinforcement learning, as a method for studying the decision-making and behavioral strategies of intelligence. It has received a lot of research and empirica...
Deep neural networks highly depend on substantial labeled samples when identifying bearing fault. However, in some practical situations, it is very difficult to collect sufficient labeled samples, which limits the application of deep neural networks ...
Financial portfolio management (PM) is one of the most applicable problems in reinforcement learning (RL) owing to its sequential decision-making nature. However, existing RL-based approaches rarely focus on scalability or reusability to adapt to the...
Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical ma...
Path integral policy improvement (PI) is known to be an efficient reinforcement learning algorithm, particularly, if the target system is a high-dimensional dynamical system. However, PI, and its existing extensions, have adjustable parameters, on wh...
Many everyday activities are sequential in nature. That is, they can be seen as a sequence of subactions and sometimes subgoals. In the motor execution of sequential action, context effects are observed in which later subactions modulate the executio...
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...