mlsanityData to 14 Oct 2026

OffRIPP: Offline RL-based Informative Path Planning

arXiv 2409.16830 ↗First posted 25 Sep 2024cs.RO cs.AIcited by 1

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

Informative path planning (IPP) is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment while adhering to resource constraints. Reinforcement learning (RL) has been shown to be effective for IPP, however, it requires environment interactions, which are risky and expensive in practice. To address this problem, we propose an offline RL-based IPP framework that optimizes information gain without requiring real-time interaction during training, offering safety and cost-efficiency by avoiding interaction, as well as superior performance and fast computation during execution -- key advantages of RL. Our framework leverages batch-constrained reinforcement learning to mitigate extrapolation errors, enabling the agent to learn from pre-collected datasets generated by arbitrary algorithms. We validate the framework through extensive simulations and real-world experiments. The numerical results show that our framework outperforms the baselines, demonstrating the effectiveness of the proposed approach.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 15 of its 26 references that are in the map. First posted on arXiv 25 Sep 2024.

Builds on

15 of 26 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning3 Feb
  2. 2021Offline Reinforcement Learning with Implicit Q-Learning12 Oct
  3. 2021A Minimalist Approach to Offline Reinforcement Learning12 Jun
  4. 2020Conservative Q-Learning for Offline Reinforcement Learning8 Jun

Other references · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  5. 2018An informative path planning framework for UAV-based terrain monitoringcited by 24
  6. 2019Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Searchcited by 18
  7. 2021Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensingcited by 14
  8. 2024Learning-based Methods for Adaptive Informative Path Planningcited by 10
  9. 2023An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mappingcited by 8
  10. 2024Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planningcited by 7
  11. 2020Informative Path Planning for Mobile Sensing with Reinforcement Learningcited by 5

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021A Workflow for Offline Model-Free Robotic Reinforcement Learning
  2. 2025Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models
  3. 2025A Tutorial: An Intuitive Explanation of Offline Reinforcement Learning Theory
  4. 2022Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
  5. 2022Offline Reinforcement Learning for Visual Navigation
  6. 2021Model-Based Offline Planning with Trajectory Pruning
  7. 2020Model-Based Offline Planning
  8. 2023Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments