Artificial neural networks, deep-learning methods and the backpropagation algorithm form
the foundation of modern machine learning and artificial intelligence. These methods are …

Recently, model-based reinforcement learning algorithms have demonstrated remarkable
efficacy in visual input environments. These approaches begin by constructing a …

Abstract We show that Deep Neural Networks introduce two heteroscedastic Gumbel noise
sources into Q-Learning. To account for these noise sources, we propose Double Gumbel Q …

The field of deep learning has witnessed significant progress in recent times, particularly in
areas such as computer vision (CV), natural language processing (NLP), and speech. The …

Modern deep-learning systems are specialized to problem settings in which training occurs
once and then never again, as opposed to continual-learning settings in which training …

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite
its progress, current algorithms are still unsatisfactory in virtually every aspect of the …

Recent advancements in off-policy Reinforcement Learning (RL) have significantly improved
sample efficiency, primarily due to the incorporation of various forms of regularization that …

In value-based deep reinforcement learning with replay memories, the batch size parameter
specifies how many transitions to sample for each gradient update. Although critical to the …

Limitations are identified in the expressive capabilities of the deep feature extraction network
employed in deep reinforcement learning (DRL), particularly in complex scenarios …

We introduce AMAGO, an in-context Reinforcement Learning (RL) agent that uses sequence
models to tackle the challenges of generalization, long-term memory, and meta-learning …