  Although it is well known that exploration plays a key role in Reinforcement
Learning (RL), prevailing exploration strategies for continuous control tasks
in RL are mainly based on naive isotropic Gaussian noise regardless of the
causality relationship between action space and the task and consider all
dimensions of actions equally important. In this work, we propose to conduct
interventions on the primal action space to discover the causal relationship
between the action space and the task reward. We propose the method of
State-Wise Action Refined (SWAR), which addresses the issue of action space
redundancy and promote causality discovery in RL. We formulate causality
discovery in RL tasks as a state-dependent action space selection problem and
propose two practical algorithms as solutions. The first approach, TD-SWAR,
detects task-related actions during temporal difference learning, while the
second approach, Dyn-SWAR, reveals important actions through dynamic model
prediction. Empirically, both methods provide approaches to understand the
decisions made by RL agents and improve learning efficiency in action-redundant
tasks.
