Sökning: "Förstärkande inlärning"

Visar resultat 11 - 15 av 35 uppsatser innehållade orden Förstärkande inlärning.

1. 11. The effects of multistep learning in the hard-exploration problem

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

   Författare :Jacob Friman; [2022]
   Nyckelord :;

   Sammanfattning : Reinforcement learning is a machine learning field which has received revitalised interest in later years due to many success stories and advancements in deep reinforcement learning. A key part in reinforcement learning is the need for exploration of the environment so the agent can properly learn the best policy. LÄS MER

3. 12. Exploring the effects of state-action space complexity on training time for AlphaZero agents

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

   Författare :Tobias Glimmerfors; [2022]
   Nyckelord :Deep learning; Reinforcement learning; AlphaZero; Monte-Carlo tree search; Environment complexity; Djupinlärning; Förstärkande inlärning; AlphaZero; Monte-Carlo tree search; spelkomplexitet;

   Sammanfattning : DeepMind’s development of AlphaGo took the world by storm in 2016 when it became the first computer program to defeat a world champion at the game of Go. Through further development, DeepMind showed that the underlying algorithm could be made more general, and applied to a large set of problems. LÄS MER

4. 13. Deep Reinforcement Learning for Temperature Control in Buildings and Adversarial Attacks

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

   Författare :Kevin Ammouri; [2021]
   Nyckelord :Deep Reinforcement Learning; Adversarial Attacks; Optimal Attacks; Building Control; Optimal Control; Energy Efficiency; Djup förstärkande inlärning; Adversarial Attacker; Optimala Attacker; Byggnadskontroll; Optimal Kontroll; Energieffektivitet;

   Sammanfattning : Heating, Ventilation and Air Conditioning (HVAC) systems in buildings are energy consuming and traditional methods used for building control results in energy losses. The methods cannot account for non-linear dependencies in the thermal behaviour. LÄS MER

5. 14. Adaptive network selection for moving agents using deep reinforcement learning

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

   Författare :William Skagerström; [2021]
   Nyckelord :;

   Sammanfattning : With the rapid development and deployment of “Internet of Things”-devices comes a new era of benefits to increase the efficiency of our everyday lives. Many of these devices rely on having an established network connection in order to operate at peak performance, but this requirement could be hard to guarantee in the face of less supported infrastructure in certain parts of the world. LÄS MER

7. 15. Deep Reinforcement Learning for the Popular Game tag

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

   Författare :August Söderlund; Gustav von Knorring; [2021]
   Nyckelord :Reinforcement Learning; Neural Networks; Qlearning; Deep Q-learning; Double Deep Q-learning; Dual-agent Training.;

   Sammanfattning : Reinforcement learning can be compared to howhumans learn – by interaction, which is the fundamental conceptof this project. This paper aims to compare three differentlearning methods by creating two adversarial reinforcementlearning models and simulate them in the game tag. LÄS MER