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

arXiv: https://arxiv.org/abs/1805.01956 · HTML: https://mlsanity.com/paper/1805.01956/

## Key facts

- Cited by 111 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2018.

## Built on by (most cited first, 30 of 111)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
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- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [SACSoN: Scalable Autonomous Control for Social Navigation](https://mlsanity.com/paper/2306.01874/) (2023)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [Contraction Theory for Nonlinear Stability Analysis and Learning-based Control: A Tutorial Overview](https://mlsanity.com/paper/2110.00675/) (2021)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [Learning Selective Communication for Multi-Agent Path Finding](https://mlsanity.com/paper/2109.05413/) (2021)
- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions](https://mlsanity.com/paper/2402.01968/) (2024)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility](https://mlsanity.com/paper/2407.08725/) (2024)
- [Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion](https://mlsanity.com/paper/2111.00993/) (2021)
- [N2M2: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments](https://mlsanity.com/paper/2206.08737/) (2022)
- [State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2207.04361/) (2022)
- [Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems](https://mlsanity.com/paper/2104.03616/) (2021)
- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach](https://mlsanity.com/paper/2102.12668/) (2021)
- [Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty](https://mlsanity.com/paper/2110.08607/) (2021)
- [Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment](https://mlsanity.com/paper/2210.08487/) (2022)
- [NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning](https://mlsanity.com/paper/2403.06828/) (2024)
- [HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments](https://mlsanity.com/paper/2411.12150/) (2024)
- [NavDreams: Towards Camera-Only RL Navigation Among Humans](https://mlsanity.com/paper/2203.12299/) (2022)
- [SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation](https://mlsanity.com/paper/2403.00991/) (2024)
- [Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control](https://mlsanity.com/paper/2502.03640/) (2025)
- [Human-Inspired Multi-Agent Navigation using Knowledge Distillation](https://mlsanity.com/paper/2103.10000/) (2021)
- [Robot Navigation in a Crowd by Integrating Deep Reinforcement Learning and Online Planning](https://mlsanity.com/paper/2102.13265/) (2021)
- [Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators](https://mlsanity.com/paper/2104.03663/) (2021)

## 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.