Search Results for author:
Found 15 papers, 4 papers with code
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning
To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT).
Quantum Advantage Actor-Critic for Reinforcement Learning
In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits by substituting parts of the classical components.
Aquarium: A Comprehensive Framework for Exploring Predator-Prey Dynamics through Multi-Agent Reinforcement Learning Algorithms
Recent advances in Multi-Agent Reinforcement Learning have prompted the modeling of intricate interactions between agents in simulated environments.
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
Current state-of-the-art approaches for generalization apply data augmentation techniques to increase the diversity of training data.
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms.
Capturing Dependencies within Machine Learning via a Formal Process Model
We define a comprehensive SD process model for ML that encompasses most tasks and artifacts described in the literature in a consistent way.
VAST: Value Function Factorization with Variable Agent Sub-Teams
We evaluate VAST in three multi-agent domains and show that VAST can significantly outperform state-of-the-art VFF, when the number of agents is sufficiently large.
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement Learning
This paper considers sustainable and cooperative behavior in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Acoustic Leak Detection in Water Networks
In this work, we present a general procedure for acoustic leak detection in water networks that satisfies multiple real-world constraints such as energy efficiency and ease of deployment.
Analysis of Feature Representations for Anomalous Sound Detection
In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature extractors for anomalous sound detection.
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
In industrial applications, the early detection of malfunctioning factory machinery is crucial.
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
We discuss the synergetic connection between quantum computing and artificial intelligence.
Soccer Team Vectors
In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space.
Deep Neural Baselines for Computational Paralinguistics
Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE).
