  The large action space is one fundamental obstacle to deploying Reinforcement
Learning methods in the real world. The numerous redundant actions will cause
the agents to make repeated or invalid attempts, even leading to task failure.
Although current algorithms conduct some initial explorations for this issue,
they either suffer from rule-based systems or depend on expert demonstrations,
which significantly limits their applicability in many real-world settings. In
this work, we examine the theoretical analysis of what action can be eliminated
in policy optimization and propose a novel redundant action filtering
mechanism. Unlike other works, our method constructs the similarity factor by
estimating the distance between the state distributions, which requires no
prior knowledge. In addition, we combine the modified inverse model to avoid
extensive computation in high-dimensional state space. We reveal the underlying
structure of action spaces and propose a simple yet efficient redundant action
filtering mechanism named No Prior Mask (NPM) based on the above techniques. We
show the superior performance of our method by conducting extensive experiments
on high-dimensional, pixel-input, and stochastic problems with various action
redundancy. Our code is public online at https://github.com/zhongdy15/npm.
