Learning Action-based Representations Using Invariance
<|reference_start|>Learning Action-based Representations Using Invariance: Robust reinforcement learning agents using high-dimensional observations must be able to identify relevant state features amidst many exogeneous distractors. A representation that captures controllability identifies these state elements by determining what affects agent control. While methods such as inverse dynamics and mutual information capture controllability for a limited number of timesteps, capturing long-horizon elements remains a challenging problem. Myopic controllability can capture the moment right before an agent crashes into a wall, but not the control-relevance of the wall while the agent is still some distance away. To address this we introduce action-bisimulation encoding, a method inspired by the bisimulation invariance pseudometric, that extends single-step controllability with a recursive invariance constraint. By doing this, action-bisimulation learns a multi-step controllability metric that smoothly discounts distant state features that are relevant for control. We demonstrate that action-bisimulation pretraining on reward-free, uniformly random data improves sample efficiency in several environments, including a photorealistic 3D simulation domain, Habitat. Additionally, we provide theoretical analysis and qualitative results demonstrating the information captured by action-bisimulation.<|reference_end|>
@article{rudolph2024learning,
    title={Learning Action-based Representations Using Invariance},
    author={Max Rudolph, Caleb Chuck, Kevin Black, Misha Lvovsky, Scott Niekum,
  Amy Zhang},
    journal={arXiv preprint arXiv:2403.16369},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16369},
    primaryClass={cs.LG cs.AI stat.ML}
}