Sökning: "Explainable Reinforcement Learning"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Explainable Reinforcement Learning.

1. 1. Explainable AI for Multi-Agent Control Problem

   Master-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Hanna Prokopova; [2023]
   Nyckelord :;

   Sammanfattning : This report presents research on the application of policy explanation techniques in the context of coordinated reinforcement learning (CRL) for mobile network optimization. The goal was to improve the interpretability and comprehensibility of decision-making processes in multi-agent environments, with a particular focus on the Remote Antenna Tilt (RET) problem. LÄS MER

3. 2. Playstyle Generation with Multimodal Generative Adversarial Imitation Learning : Style-reward from Human Demonstration for Playtesting Agents

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :William Ahlberg; [2023]
   Nyckelord :Imitation Learning; Reinforcement Learning; Game-testing; Imitationsinlärning; Förstärkande inlärning; Speltestning;

   Sammanfattning : Playtesting plays a crucial role in video game production. The presence of gameplay issues and faulty design choices can be of great detriment to the overall player experience. LÄS MER

4. 3. Explainable Reinforcement Learning for Gameplay

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Àlex Costa Sánchez; [2022]
   Nyckelord :Explainable Artificial Intelligence; Local Interpretable Model-agnostic Explanations; Reinforcement Learning; Shapley Additive Explanations; Intel·ligencia Artificial Interpretable; Explicacions model-agnòstiques localment interpretables; Aprenentatge per reforç; Explicacions additives de Shapley; Förklarbar artificiell intelligent; Lokala tolkningsbara modellagnostiska förklaringar; Förstärkningsinlärning; Shapleys additiv förklaringar;

   Sammanfattning : State-of-the-art Machine Learning (ML) algorithms show impressive results for a myriad of applications. However, they operate as a sort of a black box: the decisions taken are not human-understandable. LÄS MER

5. 4. XAI-assisted Radio Resource Management: Feature selection and SHAP enhancement

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Nicolás Sibuet Ruiz; [2022]
   Nyckelord :Deep Learning; Explainable Artficial Intelligence; 5G; Model Reduction; Deep Learning; Förklarlig AI; 5G; Modellreduktion;

   Sammanfattning : With the fast development of radio technologies, wireless systems have become more convoluted. This complexity, accompanied by an increase of the number of connections, is translated into a need for more parameters to analyse and decisions to take at each instant. LÄS MER

7. 5. Explainable Reinforcement Learning for Remote Electrical Tilt Optimization

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Artin Mirzaian; [2022]
   Nyckelord :Reinforcement Learning; Explainability; Explainable Reinforcement Learning; Machine Learning; Artificial Intelligence; Remote Electrical tilt optimization.; Förstärkningsinlärning; Förklarbarhet; Förklarbar Förstärkningsinlärning; Maskininlärning; Artificiell Intelligens; Optimering av Fjärrlutning.;

   Sammanfattning : Controlling antennas’ vertical tilt through Remote Electrical Tilt (RET) is an effective method to optimize network performance. Reinforcement Learning (RL) algorithms such as Deep Reinforcement Learning (DRL) have been shown to be successful for RET optimization. LÄS MER