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.