Search Results for author:
Found 13 papers, 1 papers with code
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.
Deep set conditioned latent representations for action recognition
The proposed method achieves an improvement of around 1. 49% mAP in atomic action recognition and 17. 57% mAP in composite action recognition, over a I3D-NL baseline, on the CATER dataset.
Ranked #1 on
Atomic action recognition
on CATER
(using extra training data)
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
Task Independent Capsule-Based Agents for Deep Q-Learning
In this paper, we propose DCapsQN, a task-independent CapsNets-based architecture in the deep reinforcement learning setting.
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.
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment
As an alternative to the widespread method of a weighted sum of rewards, Explore Options let the agent call an intrinsically motivated agent in order to observe and learn from interesting behaviors in the environment.
Exploiting non-i.i.d. data towards more robust machine learning algorithms
The resulting algorithm favours correlations that are universal over the subpopulations and indeed a better performance is obtained on an out-of-distribution test set with respect to a more conventional l_2-regularization.
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.
