We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence
modeling problem. This allows us to draw upon the simplicity and scalability of the …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

The Dreamer agent provides various benefits of Model-Based Reinforcement Learning
(MBRL) such as sample efficiency, reusable knowledge, and safe planning. However, its …

Many recent artificial intelligence (AI) systems take inspiration from biological episodic
memory. Here, we ask how these 'episodic-inspired'AI systems might inform our …

Effective decision making involves flexibly relating past experiences and relevant contextual
information to a novel situation. In deep reinforcement learning (RL), the dominant paradigm …

The ability to leverage heterogeneous robotic experience from different robots and tasks to
quickly master novel skills and embodiments has the potential to transform robot learning …

Reinforcement learning agents often forget details of the past, especially after delays or
distractor tasks. Agents with common memory architectures struggle to recall and integrate …

Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample
inefficiency. Meta-RL directly addresses this sample inefficiency by learning to perform few …

With the recent development of deep learning, AI models are widely used in various
domains. AI models show good performance for definite tasks such as image classification …

Deep learning (DL) models for tabular data problems are receiving increasingly more
attention, while the algorithms based on gradient-boosted decision trees (GBDT) remain a …