mlsanityData to 7 Oct 2026

Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments

arXiv 1612.05533 ↗First posted 16 Dec 2016cs.RO cs.AI cs.LGcited by 50

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Abstract (arXiv)

In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Dec 2016.

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Citing papers per year: 2020: 1; 2021: 14; 2022: 8; 2023: 7; 2024: 10; 2025: 4; 2026: 6.

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  1. 2021Learning One Representation to Optimize All Rewardscited by 110
  2. 2022Does Zero-Shot Reinforcement Learning Exist?cited by 79
  3. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  4. 2022Verifying Learning-Based Robotic Navigation Systemscited by 33
  5. 2023Maximum State Entropy Exploration using Predecessor and Successor Representationscited by 23
  6. 2021A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representationcited by 19
  7. 2024Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learningcited by 18
  8. 2020Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learningcited by 14
  9. 2021A New Representation of Successor Features for Transfer across Dissimilar Environmentscited by 13
  10. 2021Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigationcited by 13
  11. 2022Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planningcited by 13
  12. 2024Finer Behavioral Foundation Models via Auto-Regressive Features and Advantage Weightingcited by 12
38 more
  1. 2021Curriculum Learning for Safe Mapless Navigationcited by 11
  2. 2022Constrained Reinforcement Learning for Robotics via Scenario-Based Programmingcited by 11
  3. 2021Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigationcited by 10
  4. 2023Self-Supervised Reinforcement Learning that Transfers using Random Featurescited by 10
  5. 2024Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matchingcited by 10
  6. 2023Safe Deep Reinforcement Learning by Verifying Task-Level Propertiescited by 9
  7. 2023Composing Task Knowledge with Modular Successor Feature Approximatorscited by 7
  8. 2023Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigationcited by 7
  9. 2024Efficient Diversity-based Experience Replay for Deep Reinforcement Learningcited by 7
  10. 2021Successor Feature Sets: Generalizing Successor Representations Across Policiescited by 6
  11. 2021Successor Feature Representationscited by 6
  12. 2024SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learningcited by 5
  13. 2021XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Treescited by 3
  14. 2022Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogisticscited by 3
  15. 2022Meta Reinforcement Learning with Successor Feature Based Contextcited by 3
  16. 2025SR-Reward: Taking The Path More Traveledcited by 3
  17. 2021Deep Learning for Embodied Vision Navigation: A Surveycited by 2
  18. 2026A Reward-Free Viewpoint on Multi-Objective Reinforcement Learningcited by 2
  19. 2021Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditionscited by 1
  20. 2021Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Playercited by 1
  21. 2023Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learningcited by 1
  22. 2024Bidirectional Progressive Neural Networks with Episodic Return Progress for Emergent Task Sequencing and Robotic Skill Transfercited by 1
  23. 2024Decoupling Exploration and Exploitation for Unsupervised Pre-training with Successor Featurescited by 1
  24. 2024Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learningcited by 1
  25. 2025Which Features are Best for Successor Features?cited by 1
  26. 2025MAGIK: Mapping to Analogous Goals via Imagination-enabled Knowledge Transfercited by 1
  27. 2026Can We Really Learn One Representation to Optimize All Rewards?cited by 1
  28. 2021Transferring Domain Knowledge with an Adviser in Continuous Taskscited by 0
  29. 2022A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictionscited by 0
  30. 2022Reward-Predictive Clusteringcited by 0
  31. 2023Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Informationcited by 0
  32. 2024A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object trackingcited by 0
  33. 2024Multi-Agent Transfer Learning via Temporal Contrastive Learningcited by 0
  34. 2025Don't Just Search, Understand: Semantic Path Planning Agent for Spherical Tensegrity Robots in Unknown Environmentscited by 0
  35. 2026Spectral Alignment in Forward-Backward Representations via Temporal Abstractioncited by 0
  36. 2026ASPECT:Analogical Semantic Policy Execution via Language Conditioned Transfercited by 0
  37. 2026ATLAS: Adaptive Topological Learning with Abstract Successors for Continual Learningcited by 0
  38. 2026Reward as Observation: Learning Reward-Based Policies for Rapid Adaptationcited by 0