Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge
This addresses the challenge of data efficiency for researchers and practitioners in reinforcement learning, though it appears incremental as it builds on existing causal methods.
The paper tackles the problem of limited data collection budgets in model training by introducing causal exploration, a strategy that uses causal knowledge to improve sample efficiency and reliability in task-gnostic reinforcement learning, resulting in learning accurate world models with fewer data and providing theoretical convergence guarantees.
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.