
The Role of Human-in-the-Loop Preferences in Reward Function Learning for Humanoid Tasks
3 Dec 2024
Explore how human-in-the-loop preferences refine reward functions in tasks like humanoid running and jumping.

Tracking Reward Function Improvement with Proxy Human Preferences in ICPL
3 Dec 2024
Explore how In-Context Preference Learning (ICPL) progressively refined reward functions in humanoid tasks using proxy human preferences.

Few-shot In-Context Preference Learning Using Large Language Models: Environment Details
3 Dec 2024
Discover the key environment details, task descriptions, and metrics for 9 tasks in IsaacGym, as outlined in this paper.

ICPL Baseline Methods: Disagreement Sampling and PrefPPO for Reward Learning
3 Dec 2024
Learn how disagreement sampling and PrefPPO optimize reward learning in reinforcement learning.

Few-shot In-Context Preference Learning Using Large Language Models: Full Prompts and ICPL Details
3 Dec 2024
Full Prompts and ICPL Details for study Few-shot in-context preference learning with LLMs

How ICPL Enhances Reward Function Efficiency and Tackles Complex RL Tasks
3 Dec 2024
ICPL enhances reinforcement learning by integrating LLMs and human preferences for efficient reward function synthesis.

Human Preferences Help Scientists Train AI 30x Faster Than Before
3 Dec 2024

How ICPL Addresses the Core Problem of RL Reward Design
3 Dec 2024
ICPL integrates LLMs with human preferences to iteratively synthesize reward functions, offering an efficient, feedback-driven approach to RL reward design.

How Do We Teach Reinforcement Learning Agents Human Preferences?
3 Dec 2024
Explore how ICPL builds on foundational works like EUREKA to redefine reward design in reinforcement learning.