Sökning: "Dueling Deep Q-Network"

Hittade 4 uppsatser innehållade orden Dueling Deep Q-Network.

1. 1. Research on Dynamic Offloading Strategy of Satellite Edge Computing Based on Deep Reinforcement Learning

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

   Författare :Rui Geng; [2021]
   Nyckelord :Deep reinforcement learning; Satellite edge computing; Offloading strategy; Djup Förstärkning Lärande; Satellit Kant Datoranvändning; Avlastning Strategi;

   Sammanfattning : Nowadays more and more data is generated at the edge of the network, and people are beginning to consider decentralizing computing tasks to the edge of the network. The network architecture of edge computing is different from the traditional network architecture. LÄS MER

3. 2. Deep reinforcement learning for real-time power grid topology optimization

   Kandidat-uppsats, Lunds universitet/Matematisk statistik

   Författare :Jacob Rothschild; [2021]
   Nyckelord :Deep reinforcement learning; Dueling Deep Q-Network; electricity transmission network; real-time topology optimization; sustainable energy; Mathematics and Statistics;

   Sammanfattning : In our pursuit of carbon neutrality, drastic changes to generation and consumption of electricity will cause new and complex demands on the power grid and its operators. A cheap, promising, and under-exploited mitigation is real-time power grid topology optimization (RTTO). LÄS MER

4. 3. Deep Reinforcement Learning for Autonomous Highway Driving Scenario

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

   Författare :Neil Pradhan; [2021]
   Nyckelord :Deep reinforcement learning; Highway driving scenario; Tactical decision making; fuel reduction; high-level decision making; autonomous driving; Partially Observable Markov Decision Process POMDP .; Lärande om djupförstärkning; motorvägsscenario; taktiskt beslutsfattande; bränslereduktion; beslut på hög nivå; autonom körning; Partially Observable Markov Decision Process POMDP ;

   Sammanfattning : We present an autonomous driving agent on a simulated highway driving scenario with vehicles such as cars and trucks moving with stochastically variable velocity profiles. The focus of the simulated environment is to test tactical decision making in highway driving scenarios. LÄS MER

5. 4. A deep reinforcement learning approach to the problem of golf using an agent limited by human data

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

   Författare :Fredrik Omstedt; [2020]
   Nyckelord :;

   Sammanfattning : In the sport of golf, the use of statistics has become prominent as a way of understanding and improving golfers’ golf swings. Even though swing data is easily accessible thanks to a variety of technological tools, it is not always clear how to use this data, especially for amateur golfers. LÄS MER