Month: December 2024

The Role of Human-in-the-Loop Preferences in Reward Function Learning for Humanoid Tasks

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.6 HUMAN-IN-THE-LOOP PREFERENCE A.6.1 ISAACGYM TASKS We evaluate human-in-the-loop preference experiments on tasks in IsaacGym, including Quadcopter, … Read more

Tracking Reward Function Improvement with Proxy Human Preferences in ICPL

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.5 PROXY HUMAN PREFERENCE A.5.1 ADDITIONAL RESULTS Due to the high variance in LLMs performance, we report … Read more

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

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.4 ENVIRONMENT DETAILS In Table 4, we present the observation and action dimensions, along with the task … Read more

ICPL Baseline Methods: Disagreement Sampling and PrefPPO for Reward Learning

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A.3 BASELINE DETAILS To sample trajectories for reward learning, we employ the disagreement sampling scheme from (Lee … Read more

Few-shot In-Context Preference Learning Using Large Language Models: Full Prompts and ICPL Details

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference A APPENDIX We would suggest visiting https://sites.google.com/view/few-shot-icpl/home for more information and videos. A.1 FULL PROMPTS A.2 ICPL … Read more

How ICPL Enhances Reward Function Efficiency and Tackles Complex RL Tasks

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference 6 CONCLUSION Our proposed method, In-Context Preference Learning (ICPL), demonstrates significant potential for addressing the challenges of … Read more

Scientists Use Human Preferences to Train AI Agents 30x Faster

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference 5 EXPERIMENTS In this section, we conducted two sets of experiments to evaluate the effectiveness of our … Read more

How ICPL Addresses the Core Problem of RL Reward Design

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference 4 METHOD Our proposed method, In-Context Preference Learning (ICPL), integrates LLMs with human preferences to synthesize reward … Read more

How Do We Teach Reinforcement Learning Agents Human Preferences?

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference 3 PROBLEM DEFINITION Our goal is to design a reward function that can be used to train … Read more

Hacking Reinforcement Learning with a Little Help from Humans (and LLMs)

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Table of Links Abstract and Introduction Related Work Problem Definition Method Experiments Conclusion and References A. Appendix A.1. Full Prompts and A.2 ICPL Details A. 3 Baseline Details A.4 Environment Details A.5 Proxy Human Preference A.6 Human-in-the-Loop Preference 2 RELATED WORK Reward Design. In reinforcement learning, reward design is a core challenge, as rewards must … Read more

Researchers Uncover Breakthrough in Human-In-the-Loop AI Training with ICPL

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:::info Authors: (1) Chao Yu, Tsinghua University; (2) Hong Lu, Tsinghua University; (3) Jiaxuan Gao, Tsinghua University; (4) Qixin Tan, Tsinghua University; (5) Xinting Yang, Tsinghua University; (6) Yu Wang, with equal advising from Tsinghua University; (7) Yi Wu, with equal advising from Tsinghua University and the Shanghai Qi Zhi Institute; (8) Eugene Vinitsky, with … Read more

What Are Gyrocommutative Gyrogroups?

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Table of Links Abstract and 1. Introduction Preliminaries Proposed Approach 3.1 Notation 3.2 Nueral Networks on SPD Manifolds 3.3 MLR in Structure Spaces 3.4 Neural Networks on Grassmann Manifolds Experiments Conclusion and References A. Notations B. MLR in Structure Spaces C. Formulation of MLR from the Perspective of Distances to Hyperplanes D. Human Action Recognition … Read more

Microsoft accuses FTC of leaking news of its antitrust investigation

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Illustration: The Verge Microsoft is asking the inspector general at the Federal Trade Commission to investigate whether agency management improperly leaked news of its antitrust investigation into the company, and make their findings public. Bloomberg first reported that the probe was underway last week, which The Verge later confirmed. The investigation covers Microsoft’s cloud and … Read more

Namada Launches Mainnet, Introducing Shielded Cross-Chain Transactions

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ZUG, Switzerland, December 3rd, 2024/Chainwire/–Namada, the shielded asset hub enabling shielded cross-chain transactions, has officially published its genesis block, marking the launch of its mainnet. Along with the commencement of staking and governance, this marks the start of the first phase of Namada’s five stage decentralized mainnet rollout. This marks an important step forward, offering … Read more

The Limitations of GyroSpd++ and Gr-GCN++ in Human Action Recognition and Graph Embedding Tasks

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Table of Links Abstract and 1. Introduction Preliminaries Proposed Approach 3.1 Notation 3.2 Nueral Networks on SPD Manifolds 3.3 MLR in Structure Spaces 3.4 Neural Networks on Grassmann Manifolds Experiments Conclusion and References A. Notations B. MLR in Structure Spaces C. Formulation of MLR from the Perspective of Distances to Hyperplanes D. Human Action Recognition … Read more

Perennial Unveils a Novel Intent Layer For Perpetuals – Solving DeFi’s Fragmented Liquidity Problem

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NEW YORK, United States, December 3rd, 2024/Chainwire/–Perennial announced the launch of Perennial Intents, a unique intents layer for perpetual futures, designed to unify DeFi’s fragmented liquidity landscape and deliver a centralized exchange trading experience on-chain. By sourcing liquidity from on-chain and off-chain venues, Perennial Intents is delivering deeper markets, better prices, and a unified trading … Read more