Few-shot In-Context Preference Learning Using Large Language Models: Environment Details

from Hackernoon 3 months ago

In Table 4, we detail the observation and action dimensions, task descriptions, and task metrics for nine distinct tasks implemented in IsaacGym, providing a structured overview essential for understanding agent training.
Hackernoonhttps://hackernoon.com/few-shot-in-context-preference-learning-using-large-language-models-environment-details

The tasks highlighted in Table 4 are designed to challenge different aspects of the agents' capabilities, ensuring a comprehensive evaluation of performance across various dimensions.
Hackernoonhttps://hackernoon.com/few-shot-in-context-preference-learning-using-large-language-models-environment-details

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#isaacgym #task-evaluation #observation-dimensions #action-dimensions #agent-training

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Few-shot In-Context Preference Learning Using Large Language Models: Environment Details | HackerNoonFew-shot In-Context Preference Learning Using Large Language Models: Environment Details | HackerNoon Briefly

Few-shot In-Context Preference Learning Using Large Language Models: Environment Details | HackerNoon
Few-shot In-Context Preference Learning Using Large Language Models: Environment Details | HackerNoon
Briefly