This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

Selecting exploratory actions that generate a rich stream of experience for better learning is
a fundamental challenge in reinforcement learning (RL). An approach to tackle this problem …

In standard reinforcement learning setups, the agent receives observations and performs
actions at evenly spaced intervals. However, in many real-world settings, observations are …

To achieve the ambitious goals of artificial intelligence, reinforcement learning must include
planning with a model of the world that is abstract in state and time. Deep learning has made …

Offline reinforcement learning (RL) holds promise as a means to learn high-reward policies
from a static dataset, without the need for further environment interactions. However, a key …

Building future wireless systems that support services like digital twins (DTs) is challenging
to achieve through advances to conventional technologies like meta-surfaces. While artificial …

We introduce a principled method for performing zero-shot transfer in reinforcement learning
(RL) by exploiting approximate models of the environment. Zero-shot transfer in RL has …

The options framework yields empirical success in long-horizon planning problems of
reinforcement learning (RL). Recent works show that options help improve the sample …

For autonomous vehicles, transfer learning can enhance performance by making better use
of previously learned knowledge in newly encountered scenarios, which holds great …

We investigate the impact of auxiliary learning tasks such as observation reconstruction and
latent self-prediction on the representation learning problem in reinforcement learning. We …