Search Results for author:
Found 13 papers, 2 papers with code
Player Pressure Map -- A Novel Representation of Pressure in Soccer for Evaluating Player Performance in Different Game Contexts
We propose a player pressure map to represent a given game scene, which lowers the dimension of raw data and still contains rich contextual information.
Fully Independent Communication in Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems.
Learning Independently from Causality in Multi-Agent Environments
Multi-Agent Reinforcement Learning (MARL) comprises an area of growing interest in the field of machine learning.
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments
Although still in simulation, the investigated situations are conceptually closer to real scenarios and thus, with these results, we intend to motivate further research in the subject.
Discovering Causality for Efficient Cooperation in Multi-Agent Environments
In this paper, we investigate the applications of causality in MARL and how it can be applied in MARL to penalise these lazy agents.
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football
The experiment results in the GRF environment prove that our reward shaping method is a useful addition to state-of-the-art MARL algorithms for training agents in environments with sparse reward signal.
Causality Detection for Efficient Multi-Agent Reinforcement Learning
When learning a task as a team, some agents in Multi-Agent Reinforcement Learning (MARL) may fail to understand their true impact in the performance of the team.
Deep Generative Multi-Agent Imitation Model as a Computational Benchmark for Evaluating Human Performance in Complex Interactive Tasks: A Case Study in Football
Then the trained Conditional VRNN Model is used as a benchmark to evaluate team performance.
Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks
This high-level sketch output from first stage and a text descriptor is used as an input to the conditional GAN network.
Residual Q-Networks for Value Function Factorizing in Multi-Agent Reinforcement Learning
The performance of the proposed method is compared against several state-of-the-art techniques such as QPLEX, QMIX, QTRAN and VDN, in a range of multi-agent cooperative tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Critical Analysis of Patch Similarity Based Image Denoising Algorithms
Most of the algorithms for image denoising has focused on the paradigm of non-local similarity, where image blocks in the neighborhood that are similar, are collected to build a basis for reconstruction.
Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset
In this paper, we present a novel, multi-modal sensor dataset that encompasses nine indoor activities, performed by 16 participants, and captured by four types of sensors that are commonly used in indoor applications and autonomous vehicles.
Ranked #1 on
Multimodal Activity Recognition
on LboroHAR
Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots
A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of autonomous vehicles.
