Recently, deep neural networks (DNNs) promote mainly by network architectures and loss
functions; however, the development of neuron models has been quite limited. In this study …

Network intrusion detection focuses on classifying network traffic as either normal or attack
carrier. The classification is based on information extracted from the network flow packets …

A fundamental assumption of reinforcement learning in Markov decision processes (MDPs)
is that the relevant decision process is, in fact, Markov. However, when MDPs have rich …

We focus on the task of approximating the optimal value function in deep reinforcement
learning. This iterative process is comprised of solving a sequence of optimization problems …

In this paper, we propose a novel Reinforcement Learning (RL) framework for problems with
continuous action spaces: Action Quantization from Demonstrations (AQuaDem). The …

Abstract Provably sample-efficient Reinforcement Learning (RL) with rich observations and
function approximation has witnessed tremendous recent progress, particularly when the …

Offline reinforcement learning (RL) aims to learn policies from static datasets. The value
overestimation of out-of-distribution (OOD) actions makes it difficult to directly apply general …

With the increasing presence of robots in our everyday environments, improving their social
skills is of utmost importance. Nonetheless, social robotics still faces many challenges. One …

Optimistic initialization underpins many theoretically sound exploration schemes in tabular
domains; however, in the deep function approximation setting, optimism can quickly …

We present Q-functionals, an alternative architecture for continuous control deep
reinforcement learning. Instead of returning a single value for a state-action pair, our network …