mlsanityData to 7 Oct 2026

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

arXiv 1805.01956 ↗First posted 4 May 2018cs.RO cs.AI cs.LGcited by 101

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Abstract (arXiv)

Robots that navigate among pedestrians use collision avoidance algorithms to enable safe and efficient operation. Recent works present deep reinforcement learning as a framework to model the complex interactions and cooperation. However, they are implemented using key assumptions about other agents' behavior that deviate from reality as the number of agents in the environment increases. This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules. This work also introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size. The proposed algorithm outperforms our previous approach in simulation as the number of agents increases, and the algorithm is demonstrated on a fully autonomous robotic vehicle traveling at human walking speed, without the use of a 3D Lidar.

Key facts. Cited by 101 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 May 2018.

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Citing papers per year: 2021: 29; 2022: 16; 2023: 18; 2024: 19; 2025: 10; 2026: 9.

101 papers.

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  1. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  2. 2021Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphscited by 144
  3. 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 71
  4. 2023SACSoN: Scalable Autonomous Control for Social Navigationcited by 60
  5. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  6. 2023Principles and Guidelines for Evaluating Social Robot Navigation Algorithmscited by 52
  7. 2022Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graphcited by 46
  8. 2021Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overviewcited by 42
  9. 2021Learning Selective Communication for Multi-Agent Path Findingcited by 32
  10. 2021Learning a State Representation and Navigation in Cluttered and Dynamic Environmentscited by 30
  11. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 27
  12. 2024A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directionscited by 27
38 more
  1. 2024MetaUrban: An Embodied AI Simulation Platform for Urban Micromobilitycited by 19
  2. 2021Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusioncited by 17
  3. 2022N2M2: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environmentscited by 15
  4. 2022State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersectionscited by 15
  5. 2021Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approachcited by 13
  6. 2021Conflict Avoidance in Social Navigation -- a Surveycited by 13
  7. 2021Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systemscited by 12
  8. 2021Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertaintycited by 11
  9. 2022Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environmentcited by 9
  10. 2024SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigationcited by 8
  11. 2021Human-Inspired Multi-Agent Navigation using Knowledge Distillationcited by 7
  12. 2022NavDreams: Towards Camera-Only RL Navigation Among Humanscited by 7
  13. 2024NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learningcited by 7
  14. 2025Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Controlcited by 7
  15. 2021Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogatecited by 6
  16. 2024HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environmentscited by 6
  17. 2021Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generatorscited by 5
  18. 2022Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision riskcited by 5
  19. 2023Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfindingcited by 5
  20. 2023TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Datacited by 5
  21. 2024Identifying Policy Gradient Subspacescited by 5
  22. 2024Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learningcited by 5
  23. 2024Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Spacecited by 5
  24. 2021Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planningcited by 4
  25. 2021Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigationcited by 4
  26. 2024Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigationcited by 4
  27. 2024Arena 4.0: A Comprehensive ROS2 Development and Benchmarking Platform for Human-centric Navigation Using Generative-Model-based Environment Generationcited by 4
  28. 2025Solving Multi-Agent Safe Optimal Control with Distributed Epigraph Form MARLcited by 4
  29. 2021Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraintscited by 3
  30. 2021XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Treescited by 3
  31. 2022Spatial-temporal recurrent reinforcement learning for autonomous shipscited by 3
  32. 2023Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learningcited by 3
  33. 2024Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learningcited by 3
  34. 2021Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Modelcited by 2
  35. 2021A Simple Approach to Continual Learning by Transferring Skill Parameterscited by 2
  36. 2022Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policiescited by 2
  37. 2022SoLo T-DIRL: Socially-Aware Dynamic Local Planner based on Trajectory-Ranked Deep Inverse Reinforcement Learningcited by 2
  38. 2022Harfang3D Dog-Fight Sandbox: A Reinforcement Learning Research Platform for the Customized Control Tasks of Fighter Aircraftscited by 2

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