# No Prior Mask: Eliminate Redundant Action for Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2312.06258 · HTML: https://mlsanity.com/paper/2312.06258/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 27 of 27)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning](https://mlsanity.com/paper/1810.08272/) (2018)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [Mastering Complex Control in MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1912.09729/) (2019)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Action Branching Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1711.08946/) (2017)
- [Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2106.03400/) (2021)
- [Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning](https://mlsanity.com/paper/1809.02121/) (2018)
- [Learning Action Representations for Reinforcement Learning](https://mlsanity.com/paper/1902.00183/) (2019)
- [Discrete Sequential Prediction of Continuous Actions for Deep RL](https://mlsanity.com/paper/1705.05035/) (2017)
- [Offline Reinforcement Learning with Value-based Episodic Memory](https://mlsanity.com/paper/2110.09796/) (2021)
- [The Natural Language of Actions](https://mlsanity.com/paper/1902.01119/) (2019)
- [Solving Continuous Control via Q-learning](https://mlsanity.com/paper/2210.12566/) (2022)
- [Learning to Represent Action Values as a Hypergraph on the Action Vertices](https://mlsanity.com/paper/2010.14680/) (2020)
- [Continuous Control with Action Quantization from Demonstrations](https://mlsanity.com/paper/2110.10149/) (2021)
- [Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning](https://mlsanity.com/paper/2105.13807/) (2021)
- [Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning](https://mlsanity.com/paper/1705.07269/) (2017)
- [Independently Controllable Features](https://mlsanity.com/paper/1703.07718/) (2017)
- [Action Priors for Large Action Spaces in Robotics](https://mlsanity.com/paper/2101.04178/) (2021)
- [Action Redundancy in Reinforcement Learning](https://mlsanity.com/paper/2102.11329/) (2021)
- [Policy Gradient Methods in the Presence of Symmetries and State Abstractions](https://mlsanity.com/paper/2305.05666/) (2023)
- [CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/2210.09496/) (2022)

## Built on by (most cited first, 6 of 6)

- [Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking](https://mlsanity.com/paper/2406.03704/) (2024)
- [Enhancing Diversity in Parallel Agents: A Maximum State Entropy Exploration Story](https://mlsanity.com/paper/2505.01336/) (2025)
- [STAIR: Addressing Stage Misalignment through Temporal-Aligned Preference Reinforcement Learning](https://mlsanity.com/paper/2509.23802/) (2025)
- [Overcoming Valid Action Suppression in Unmasked Policy Gradient Algorithms](https://mlsanity.com/paper/2603.09090/) (2026)
- [Reducing Action Space for Deep Reinforcement Learning via Causal Effect Estimation](https://mlsanity.com/paper/2501.14543/) (2025)
- [Where to Intervene: Action Selection in Deep Reinforcement Learning](https://mlsanity.com/paper/2507.04187/) (2025)

## Abstract (arXiv)

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.