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    Hindsight Experience Replay (HER) in multigoal reinforcement learning (RL) can introduce bias. A new algorithm, bias-corrected HER (BHER), addresses this by correcting the learning objective, improving performance in robotics tasks.

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    Area of Science:

    • Artificial Intelligence
    • Robotics
    • Machine Learning

    Background:

    • Multigoal reinforcement learning (RL) enhances standard RL with goal-conditional policies.
    • Hindsight Experience Replay (HER) is an efficient multigoal RL algorithm that uses failed experiences to provide frequent rewards.

    Purpose of the Study:

    • To analyze the bias introduced by HER's assumption that hindsight goals do not alter transition likelihoods.
    • To propose a novel algorithm, bias-corrected HER (BHER), to address and correct this hindsight bias.

    Main Methods:

    • Analysis of the likelihood changes in transitions and trajectories when using hindsight goals in RL.
    • Development and implementation of the bias-corrected HER (BHER) algorithm to rectify the learning objective.

    Main Results:

    • Demonstrated that HER's core assumption leads to a biased learning objective in multigoal RL.
    • Empirical evidence showing BHER outperforms existing state-of-the-art multigoal RL methods.

    Conclusions:

    • The assumption in HER regarding hindsight goals introduces a significant bias in multigoal RL.
    • BHER offers an effective solution to mitigate hindsight bias, enhancing performance in complex robotics applications.