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Two desiderata of reinforcement learning (RL) algorithms are the ability tolearn from relatively little experience and the ability to learn policies thatgeneralize to a range of problem specifications. In factored state spaces, oneapproach towards achieving both goals is to learn state abstractions, whichonly keep the necessary variables for learning the tasks at hand. This paperintroduces Causal Bisimulation Modeling (CBM), a method that learns the causalrelationships in the dynamics and reward functions for each task to derive aminimal, task-specific abstraction. CBM leverages and improves implicitmodeling to train a high-fidelity causal dynamics model that can be reused forall tasks in the same environment. Empirical validation on manipulationenvironments and Deepmind Control Suite reveals that CBM's learned implicitdynamics models identify the underlying causal relationships and stateabstractions more accurately than explicit ones. Furthermore, the derived stateabstractions allow a task learner to achieve near-oracle levels of sampleefficiency and outperform baselines on all tasks.

Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning