RL-SaLLM-F uses an LLM to both label trajectory preferences and generate self-augmented imagined trajectories, achieving MetaWorld success rates comparable to privileged-reward teachers without using any privileged information.
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Online Preference-based Reinforcement Learning with Self-augmented Feedback from Large Language Model
RL-SaLLM-F uses an LLM to both label trajectory preferences and generate self-augmented imagined trajectories, achieving MetaWorld success rates comparable to privileged-reward teachers without using any privileged information.