Abstract We study ObjectGoal Navigation--where a virtual robot situated in a new
environment is asked to navigate to an object. Prior work has shown that imitation learning …

We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has
access to an offline dataset and the ability to collect experience via real-world online …

Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …

Imitation learning holds tremendous promise in learning policies efficiently for complex
decision making problems. Current state-of-the-art algorithms often use inverse …

We introduce an offline multi-agent reinforcement learning (offline MARL) framework that
utilizes previously collected data without additional online data collection. Our method …

We propose VRL3, a powerful data-driven framework with a simple design for solving
challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major …

Annotation tools are an essential component in the creation of datasets for machine learning
purposes. Annotation tools have evolved greatly since the turn of the century, and now …

Abstract Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation
learning that aims to learn a reward function that rationalizes expert demonstrations …

Pre-training with offline data and online fine-tuning using reinforcement learning is a
promising strategy for learning control policies by leveraging the best of both worlds in terms …

Reinforcement Learning (RL) from Human Preference-based feedback is a popular
paradigm for fine-tuning generative models, which has produced impressive models such as …