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I currently have a database recorded from human behavioural (observation, action, reward, next action) with 400k examples collected in real-life conditions.

I want to implement a kind of pretraining function for SAC algorithm from Stable Baselines 3 but it seems Imitation API (https://imitation.readthedocs.io/en/latest/) does not correspond exactly what i'm looking for for some reasons :

  • Imitation Learning is designed to reproduce a behaviour from (observation, action) couple and does not use recorded reward.
  • AIRL and GAIL does not support variable horizons (Actually, it seems true for almost all imitation algos)
  • SQIL can be unstable with continuous action spaces algorithms like SAC.

I read the paper "Accelrating inverse reinforcement learning with expert boostrapping" ( arXiv:2402.02608, Wu, Sanjiban 2024, URL : https://arxiv.org/abs/2402.02608). The proposed solution seems close of what i'm looking for. To make it short the concept is based on two principles :

  • Expert Replay Boostrapping (ERB)
  • Expert Q Boostrapping (EQB)

ERB seems easy to implement because it's no more than pre-filling the replay buffer with historical data but I don't know how to implement EQB... (Maybe training critic independantly before training actor ?)

Moreover, i read somwhere feedeing an RL algo with only "good" examples (especially without cases of illegal actions) could lead to instability...

Could someone help me on this point ?

Thanks.

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