mlsanityData to 14 Oct 2026

A Validation Tool for Designing Reinforcement Learning Environments

arXiv 2112.05519 ↗First posted 10 Dec 2021cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Reinforcement learning (RL) has gained increasing attraction in the academia and tech industry with launches to a variety of impactful applications and products. Although research is being actively conducted on many fronts (e.g., offline RL, performance, etc.), many RL practitioners face a challenge that has been largely ignored: determine whether a designed Markov Decision Process (MDP) is valid and meaningful. This study proposes a heuristic-based feature analysis method to validate whether an MDP is well formulated. We believe an MDP suitable for applying RL should contain a set of state features that are both sensitive to actions and predictive in rewards. We tested our method in constructed environments showing that our approach can identify certain invalid environment formulations. As far as we know, performing validity analysis for RL problem formulation is a novel direction. We envision that our tool will serve as a motivational example to help practitioners apply RL in real-world problems more easily.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 26 references that are in the map. First posted on arXiv 10 Dec 2021.

Builds on

14 of 26 references.

References · 14

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  5. 2020Mastering Atari with Discrete World Modelscited by 1,007
  6. 2018Recurrent World Models Facilitate Policy Evolutioncited by 785
  7. 2018Top-K Off-Policy Correction for a REINFORCE Recommender Systemcited by 182
  8. 2018Dopamine: A Research Framework for Deep Reinforcement Learningcited by 122
  9. 2018Deep Reinforcement Learning for Page-wise Recommendationscited by 100
  10. 2017Device Placement Optimization with Reinforcement Learningcited by 86
  11. 2018Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Searchcited by 82
  12. 2018Horizon: Facebook's Open Source Applied Reinforcement Learning Platformcited by 48
2 more
  1. 2020Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Makingcited by 30
  2. 2020Causally Correct Partial Models for Reinforcement Learningcited by 24

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Statistical and Algorithmic Foundations of Reinforcement Learning
  2. 2026Impact of Markov Decision Process Design on Sim-to-Real Reinforcement Learning
  3. 2024Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions
  4. 2026Evaluation Metrics for Safe Reinforcement Learning
  5. 2026A Survey on the Verification of Reinforcement Learning Policies
  6. 2024Monitored Markov Decision Processes
  7. 2024Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet
  8. 2024Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration