In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …

Recent improvements in conditional generative modeling have made it possible to generate
high-quality images from language descriptions alone. We investigate whether these …

Abstract We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-
supervised image representation learning. BYOL relies on two neural networks, referred to …

In reinforcement learning (RL) research, it is common to assume access to direct online
interactions with the environment. However in many real-world applications, access to the …

In this work we identify the dormant neuron phenomenon in deep reinforcement learning,
where an agent's network suffers from an increasing number of inactive neurons, thereby …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

In mobile edge computing systems, an edge node may have a high load when a large
number of mobile devices offload their tasks to it. Those offloaded tasks may experience …

Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but
there remain significant gaps in our understanding. We therefore present a systematic and …

Value-function approximation methods that operate in batch mode have foundational
importance to reinforcement learning (RL). Finite sample guarantees for these methods …