Sökning: "Hierarchical Reinforcement Learning"

Visar resultat 1 - 5 av 7 avhandlingar innehållade orden Hierarchical Reinforcement Learning.

1. 1. Embodied Evolution of Learning Ability

   Författare :Stefan Elfwing; Henrik Christensen; Jarmo Alander; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Embodied Evolution; Evolutionary Robotics; Reinforcement Learning; Shaping Rewards; Meta-parameters; Hierarchical Reinforcement Learning; Computer science; Datalogi;

   Sammanfattning : Embodied evolution is a methodology for evolutionary robotics that mimics the distributed, asynchronous, and autonomous properties of biological evolution. The evaluation, selection, and reproduction are carried out by cooperation and competition of the robots, without any need for human intervention. LÄS MER

3. 2. Ultra-Reliable and Resilient Communication Service for Cyber-Physical Systems

   Författare :Milad Ganjalizadeh; Marina Petrova; Jens Zander; Petar Popovski; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; 5G; availability; cyber-physical systems CPSs ; deep Q-networks DQN ; deep reinforcement learning; distributed learning; machine learning; network slicing; reliability; soft actor-critic SAC ; ultra-reliable low-latency communications URLLC ; wireless communications.; Informations- och kommunikationsteknik; Information and Communication Technology; Datalogi; Computer Science;

   Sammanfattning : Cyber-Physical Systems (CPSs) are becoming ubiquitous in modern society, enabling new applications that rely on the seamless interaction between computing, communication, and physical processes. In this context, ultra-reliable low-latency communications (URLLC) emerges as a crucial element, reliably allowing the real-time exchange of critical data. LÄS MER

4. 3. Representation and Learning for Robotic Grasping, Caging, and Planning

   Författare :Johannes Andreas Stork; Danica Kragic; Wolfram Burgard; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Grasping; Caging; Planning; State Representation; Optimization; Topology; Manipulation; Reinforcement Learning; Datalogi; Computer Science;

   Sammanfattning : Robots need to grasp, handle, and manipulate objects, navigate their environment, and understand the state of the world around them. Like all artificial intelligence agents, they have to make predictions, formulate goals, reason about actions, and make plans. LÄS MER

5. 4. Towards Real-World Federated Learning: Empirical Studies in the Domain of Embedded Systems

   Författare :Hongyi Zhang; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Machine learning; Software engineering; Federated Learning;

   Sammanfattning : Context: Artificial intelligence (AI) has led a new phase of technical revolution and industrial development around the world since the twenty-first century, revolutionizing the way of production. Artificial intelligence (AI), an emerging information technology, is thriving, and AI application technologies are gaining traction, particularly in professional services such as healthcare, education, finance, security, etc. LÄS MER

7. 5. Reinforcement Learning for Improved Utility of Simulation-Based Training

   Författare :Johan Källström; Fredrik Heintz; Erik Herzog; Julian Togelius; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : Team training in complex domains often requires a substantial number of resources, e.g. vehicles, machines, and role-players. For this reason, it may be difficult to realise efficient and effective training scenarios in a real-world setting. LÄS MER