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Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
Furthermore, KALM effectively enables the LLM to comprehend environmental dynamics, resulting in the generation of meaningful imaginary rollouts that reflect novel skills and demonstrate the seamless integration of large language models and reinforcement learning.
Empowering Language Models with Active Inquiry for Deeper Understanding
The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language.
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
In this work, we focus on imitator learning based on only one expert demonstration.
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation
To ease the learning burden of the policy, we investigate an inside-out scheme for natural language-conditioned RL by developing a task language (TL) that is task-related and unique.
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