By using smart radio devices, a jammer can dynamically change its jamming policy based
on opposing security mechanisms; it can even induce the mobile device to enter a specific …

Reinforcement Learning algorithms can learn complex behavioral patterns for sequential
decision making tasks wherein an agent interacts with an environment and acquires …

In recent years, reinforcement learning (RL) has achieved remarkable success due to the
growing adoption of deep learning techniques and the rapid growth of computing power …

In the practice of sequential decision making, agents are often designed to sense state at
regular intervals of $ d $ time steps, $ d> 1$, ignoring state information in between sensing …

The physical world evolves continuously in time. Most prior works on reinforcement learning
cast continuous-time environments into a discrete-time Markov Decision Process (MDP), by …

Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective on a
wide range of challenging decision making and control tasks. However, these methods …

We propose the factorizing variational autoencoder (FAVAE), a generative model for
learning dis-entangled representations from sequential data via the information bottleneck …

Macro actions have been demonstrated to be beneficial for the learning processes of an
agent and have encouraged a variety of techniques to be developed for constructing more …

Combination of machine learning (for generating machine intelligence), computer vision (for
better environment perception), and robotic systems (for controlled environment interaction) …

As trajectories sampled by policies used by reinforcement learning (RL) and generative flow
networks (GFlowNets) grow longer, credit assignment and exploration become more …