In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

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 …

Recent work has shown that offline reinforcement learning (RL) can be formulated as a
sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via …

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general
understanding of the world. Such an agent would require the ability to continually …

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

We present a system that enables an autonomous small-scale RC car to drive aggressively
from visual observations using reinforcement learning (RL). Our system, FastRLAP, trains …

Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill
acquisition for robotic systems. However, in practice, real-world robotic RL typically requires …

Model-based reinforcement learning (RL) is a sample-efficient way of learning complex
behaviors by leveraging a learned single-step dynamics model to plan actions in …

The pre-train and fine-tune paradigm in machine learning has had dramatic success in a
wide range of domains because the use of existing data or pre-trained models on the …

One promising approach towards effective robot decision making in complex, long-horizon
tasks is to sequence together parameterized skills. We consider a setting where a robot is …