4 papers
Behavior-Invariant Task Representation Learning with Transformer-based World Models for Offline Meta-Reinforcement Learning
Fuyuan Qian, Menglong Zhang, Song Wang +1
Offline meta-reinforcement learning leverages static datasets to enable agents to generalize to unseen environments by combining offline efficiency with meta-learning adaptability,…
Memento-Skills: Let Agents Design Agents
Huichi Zhou, Siyuan Guo, Anjie Liu +14
We introduce \emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an \emph{agent-designing agent}: it autonomously constructs, adapts, and…
Learning Task Belief Similarity with Latent Dynamics for Meta-Reinforcement Learning
Menglong Zhang, Fuyuan Qian
Meta-reinforcement learning requires utilizing prior task distribution information obtained during exploration to rapidly adapt to unknown tasks. The efficiency of an agent's explo…
Memory Sequence Length of Data Sampling Impacts the Adaptation of Meta-Reinforcement Learning Agents
Menglong Zhang, Fuyuan Qian, Quanying Liu
Fast adaptation to new tasks is extremely important for embodied agents in the real world. Meta-reinforcement learning (meta-RL) has emerged as an effective method to enable fast a…