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New RE-IRL framework infers investor preferences from market actions

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to infer investor reward functions from their observed actions and market data. This approach is designed for situations where the underlying environmental dynamics are unknown or inaccessible. To handle limited data, the method employs a K-nearest neighbor technique for estimating the behavior policy and includes a statistical testing component to validate the findings. AI

IMPACT Introduces a novel IRL method for financial modeling, potentially improving algorithmic trading strategies.

RANK_REASON This is a research paper detailing a new algorithmic approach.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New RE-IRL framework infers investor preferences from market actions

COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Chen Xu ·

    Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach

    arXiv:2604.24280v1 Announce Type: new Abstract: We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment actions and market conditions. Unlike traditional IRL algorithms, RE-IRL is employed…

  2. arXiv cs.LG TIER_1 English(EN) · Chen Xu ·

    Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach

    We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment actions and market conditions. Unlike traditional IRL algorithms, RE-IRL is employed to account for environments where transition pr…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Model-Free Inference of Investor Preferences: A Relative Entropy IRL Approach

    We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment actions and market conditions. Unlike traditional IRL algorithms, RE-IRL is employed to account for environments where transition pr…

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