mlsanityData to 14 Oct 2026

Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments

arXiv 2410.14616 ↗First posted 18 Oct 2024cs.RO cs.AI cs.LGcited by 4

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Deep Reinforcement learning (DRL) is used to enable autonomous navigation in unknown environments. Most research assume perfect sensor data, but real-world environments may contain natural and artificial sensor noise and denial. Here, we present a benchmark of both well-used and emerging DRL algorithms in a navigation task with configurable sensor denial effects. In particular, we are interested in comparing how different DRL methods (e.g. model-free PPO vs. model-based DreamerV3) are affected by sensor denial. We show that DreamerV3 outperforms other methods in the visual end-to-end navigation task with a dynamic goal - and other methods are not able to learn this. Furthermore, DreamerV3 generally outperforms other methods in sensor-denied environments. In order to improve robustness, we use adversarial training and demonstrate an improved performance in denied environments, although this generally comes with a performance cost on the vanilla environments. We anticipate this benchmark of different DRL methods and the usage of adversarial training to be a starting point for the development of more elaborate navigation strategies that are capable of dealing with uncertain and denied sensor readings.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 28 of its 64 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

28 of 64 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Demonstrating Agile Flight from Pixels without State Estimation18 Jun

Other references · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  5. 2023Mastering Diverse Domains through World Modelscited by 1,253
  6. 2018Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomizationcited by 294
  7. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 238
  8. 2016Learning to Navigate in Complex Environmentscited by 198
  9. 2016ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learningcited by 179
  10. 2019Action Robust Reinforcement Learning and Applications in Continuous Controlcited by 174
  11. 2020Liquid Time-constant Networkscited by 161
  12. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 103
15 more
  1. 2022Deep Hierarchical Planning from Pixelscited by 101
  2. 2016Playing FPS Games with Deep Reinforcement Learningcited by 95
  3. 2016DeepMind Labcited by 74
  4. 2016Graying the black box: Understanding DQNscited by 68
  5. 2020Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learningcited by 61
  6. 2022Evaluating Long-Term Memory in 3D Mazescited by 43
  7. 2021Goal-Driven Autonomous Exploration Through Deep Reinforcement Learningcited by 35
  8. 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 30
  9. 2023Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustnesscited by 19
  10. 2018Online Robust Policy Learning in the Presence of Unknown Adversariescited by 15
  11. 2022Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planningcited by 13
  12. 2021Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPscited by 12
  13. 2023Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisionscited by 7
  14. 2024Understanding and Diagnosing Deep Reinforcement Learningcited by 6
  15. 2021A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehiclescited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigationcited by 3
  2. 2025Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approachcited by 1
  3. 2025Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstaclescited by 0
  4. 2026Prediction-Based Markov Violation Scores for Detecting Non-Markovian Observations in Reinforcement Learningcited by 0

Similar, not cited

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  1. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
  2. 2026RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
  3. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  4. 2024Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
  5. 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches
  6. 2024Mobile Robot Exploration Without Maps via Out-of-Distribution Deep Reinforcement Learning
  7. 2024Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
  8. 2024HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks