Designing generalizable agents capable of adapting to diverse embodiments has achieved
significant attention in Reinforcement Learning (RL), which is critical for deploying RL …

Zero-shot generalization (ZSG) to unseen dynamics is a major challenge for creating
generally capable embodied agents. To address the broader challenge, we start with the …

Zero-shot adaptation is crucial for agents facing new tasks. Offline Meta-Reinforcement
Learning (OMRL), utilizing offline multi-task datasets to train policies, offers a way to attain …

Designing a competent meta-reinforcement learning (meta-RL) algorithm in terms of data
usage remains a central challenge to be tackled for its successful real-world applications. In …

We introduce Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), a
powerful online meta-reinforcement learning approach. HTrMRL aims to address the …

The reliable deployment of deep reinforcement learning in real-world settings requires the
ability to generalize across a variety of conditions, including both in-distribution scenarios …

Traditional image retrieval methods often face challenges in adapting to varying user
preferences and dynamic datasets. To address these limitations, this research introduces a …

Reinforcement learning agents perform poorly when faced with unseen dynamics. Recent
work on Behaviour Foundation Models (BFMs) has produced agents capable of solving …

In this work, we address the challenge of zero-shot generalization (ZSG) in Reinforcement
Learning (RL), where agents must adapt to entirely novel environments without additional …

Reinforcement learning is a machine learning paradigm that allows learning through
interaction. It intertwines data collection and model training into a single problem statement …