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Found 7 papers, 0 papers with code
Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients
We are able to demonstrate the scalability of our methods for a large number of robots by employing a graph neural network (GNN) to parameterize policies for the robots.
Graph Neural Networks for Motion Planning
This paper investigates the feasibility of using Graph Neural Networks (GNNs) for classical motion planning problems.
Sufficiently Accurate Model Learning
In this paper, we investigate a method to regularize model learning techniques to provide better error characteristics for traditional control and planning algorithms.
COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS
A deep reinforcement learning solution is developed for a collaborative multiagent system.
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients
When the number of agents increases, the dimensionality of the input and control spaces increase as well, and these methods do not scale well.
Distributed Optimization
Multi-agent Reinforcement Learning
+2
Memory Augmented Control Networks
The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning.
Neural Network Memory Architectures for Autonomous Robot Navigation
This work is a first thorough study of memory structures for deep-neural-network-based robot navigation, and offers novel tools to train such networks from supervision and quantify their ability to generalize to unseen scenarios.
