Develo** an agent capable of adapting to unseen environments remains a difficult
challenge in imitation learning. This work presents Adaptive Return-conditioned Policy …

Prompt-based learning has been demonstrated as a compelling paradigm contributing to
large language models' tremendous success (LLMs). Inspired by their success in language …

This paper studies offline reinforcement learning with linear function approximation in a
setting with decision-theoretic, but not estimation sparsity. The structural restrictions of the …

In pixel-based deep reinforcement learning (DRL), learning representations of states that
change because of an agent's action or interaction with the environment poses a critical …

General-purpose agents require fine-grained controls and rich sensory inputs to perform a
wide range of tasks. However, this complexity often leads to intractable decision-making …

The information bottleneck (IB) framework formalises the essential requirement for efficient
information processing systems to achieve an optimal balance between the complexity of …

Real-world applications of reinforcement learning often involve environments where agents
operate on complex, high-dimensional observations, but the underlying (''latent'') dynamics …

Model-based reinforcement learning (MBRL) is a promising route to sample-efficient policy
optimization. However, a known vulnerability of reconstruction-based MBRL consists of …

Multi-View Reinforcement Learning (MVRL) seeks to provide agents with multi-view
observations, enabling them to perceive environment with greater effectiveness and …

Despite their stellar performance on a wide range of tasks, including in-context tasks only
revealed during inference, vanilla transformers and variants trained for next-token …