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Found 5 papers, 0 papers with code
Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning
When compared to a state-of-art algorithm for obstacle avoidance, our solution with a single escort increases navigation success up to 31%.
RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies
Through the combination of sampling-based planning, a Rapidly Exploring Randomized Tree (RRT) and an efficient kinodynamic motion planner through machine learning, we propose an efficient solution to long-range planning for kinodynamic motion planning.
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting
Robots and autonomous agents often complete goal-based tasks with limited resources, relying on imperfect models and sensor measurements.
Deep Neural Networks for Swept Volume Prediction Between Configurations
Swept Volume (SV), the volume displaced by an object when it is moving along a trajectory, is considered a useful metric for motion planning.
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology.
