  Learning dynamics models accurately is an important goal for Model-Based
Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics
model which is vulnerable to spurious correlations and therefore generalizes
poorly to unseen states. In this paper, we introduce Causal Dynamics Learning
for Task-Independent State Abstraction (CDL), which first learns a
theoretically proved causal dynamics model that removes unnecessary
dependencies between state variables and the action, thus generalizing well to
unseen states. A state abstraction can then be derived from the learned
dynamics, which not only improves sample efficiency but also applies to a wider
range of tasks than existing state abstraction methods. Evaluated on two
simulated environments and downstream tasks, both the dynamics model and
policies learned by the proposed method generalize well to unseen states and
the derived state abstraction improves sample efficiency compared to learning
without it.
