Actor-critic (AC) methods are ubiquitous in reinforcement learning. Alth...
The human body is punctuated with wide array of sensory systems that pro...
We present a classification based approach for the next best view select...
While reinforcement learning algorithms can learn effective policies for...
Real-world scenarios pose several challenges to deep learning based comp...
Implementation of variational Quantum Machine Learning (QML) algorithms ...
Spiking Neural Networks (SNN) are energy-efficient computing architectur...
While deep learning and deep reinforcement learning (RL) systems have
de...
We deal with the problem of information fusion driven satellite image/sc...
Many reinforcement learning (RL) tasks provide the agent with
high-dimen...
We present a unifying framework for designing and analysing distribution...
Deep reinforcement learning (deep RL) research has grown significantly i...
We present a framework combining hierarchical and multi-agent deep
reinf...
We present a differentiable framework capable of learning a wide variety...
Typical reinforcement learning (RL) agents learn to complete tasks speci...