Search Results for author:
Found 12 papers, 1 papers with code
SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles
To the best of our understanding, this work represents the first investigation of scene change detection in the maritime vision application.
Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning.
Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways
We demonstrate our approach on two scenarios and compare the resulting path with path planning using a Frenet frame and path planning based on a proximal policy optimization (PPO) agent.
An In-Depth Analysis of Discretization Methods for Communication Learning using Backpropagation with Multi-Agent Reinforcement Learning
We do this comparison in the context of communication learning using gradients from other agents and perform tests on several environments.
Scalability of Message Encoding Techniques for Continuous Communication Learned with Multi-Agent Reinforcement Learning
Therefore, we analyse the communication protocol used by the agents that use the mean message encoder and can conclude that the agents use a combination of an exponential and a logarithmic function in their communication policy to avoid the loss of important information after applying the mean message encoder.
Attention Based Feature Fusion For Multi-Agent Collaborative Perception
To improve the precision of object detection and alleviate limited network resources, we propose an intermediate collaborative perception solution in the form of a graph attention network (GAT).
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning
The most common approach to allow learned communication between agents is the use of a differentiable communication channel that allows gradients to flow between agents as a form of feedback.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning to Communicate with Reinforcement Learning for an Adaptive Traffic Control System
In this paper, we investigate independent Q-learning (IQL) without communication and differentiable inter-agent learning (DIAL) with learned communication on an adaptive traffic control system (ATCS).
Mixed Cooperative-Competitive Communication Using Multi-Agent Reinforcement Learning
We look at the difference in performance between communication that is private for a team and communication that can be overheard by the other team.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark
In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al.
Learning to Communicate Using Counterfactual Reasoning
The credit assignment problem, the non-stationarity of the communication environment and the creation of influenceable agents are major challenges within this research field which need to be overcome in order to learn a valid communication protocol.
