  The effectiveness of model training heavily relies on the quality of
available training resources. However, budget constraints often impose
limitations on data collection efforts. To tackle this challenge, we introduce
causal exploration in this paper, a strategy that leverages the underlying
causal knowledge for both data collection and model training. We, in
particular, focus on enhancing the sample efficiency and reliability of the
world model learning within the domain of task-agnostic reinforcement learning.
During the exploration phase, the agent actively selects actions expected to
yield causal insights most beneficial for world model training. Concurrently,
the causal knowledge is acquired and incrementally refined with the ongoing
collection of data. We demonstrate that causal exploration aids in learning
accurate world models using fewer data and provide theoretical guarantees for
its convergence. Empirical experiments, on both synthetic data and real-world
applications, further validate the benefits of causal exploration.
