Recently, diffusion model shines as a promising backbone for the sequence modeling
paradigm in offline reinforcement learning (RL). However, these works mostly lack the …

We study the offline meta-reinforcement learning (OMRL) problem, a paradigm which
enables reinforcement learning (RL) algorithms to quickly adapt to unseen tasks without any …

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to
enhance the agent's generalization ability on unseen tasks. However, the context shift …

Adapting to the changes in transition dynamics is essential in robotic applications. By
learning a conditional policy with a compact context, context-aware meta-reinforcement …

In meta reinforcement learning (meta RL), an agent seeks a Bayes-optimal policy–the
optimal policy when facing an unknown task that is sampled from some known task …

Recent work has shown that supervised pre-training on learning histories of RL algorithms
results in a model that captures the learning process and is able to improve in-context on …

As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement
learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly …

Decomposing complex real-world tasks into simpler subtasks and devising a subtask
execution plan is critical for humans to achieve effective decision-making. However …

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can
quickly adapt to new environments and tasks. In this work, we study the MRL problem under …

Meta-Reinforcement Learning (Meta-RL) agents can struggle to operate across tasks with
varying environmental features that require different optimal skills (ie, different modes of …