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Found 4 papers, 3 papers with code
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians.
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
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.
Socially Aware Motion Planning with Deep Reinforcement Learning
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e. g., passing on the right).
Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning
Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e. g. goal) is unobservable to the others.
Multiagent Systems
