Sökning: "Markov-beslutsprocess"

Visar resultat 1 - 5 av 8 uppsatser innehållade ordet Markov-beslutsprocess.

  1. 1. S-MARL: An Algorithm for Single-To-Multi-Agent Reinforcement Learning : Case Study: Formula 1 Race Strategies

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

    Författare :Marinaro Davide; [2023]
    Nyckelord :Reinforcement Learning; Single-to-Multi-Agent; Learning Stability; Exploration-Exploitation trade-off; Race Strategy Optimization; Förstärkningsinlärning; Från en till flera agenter; Stabilitet vid inlärning; Utforskning-exploatering; Optimering av tävlingsstrategier;

    Sammanfattning : A Multi-Agent System is a group of autonomous, intelligent, interacting agents sharing an environment that they observe through sensors, and upon which they act with actuators. The behaviors of these agents can be either defined upfront by programmers or learned by trial-and-error resorting to Reinforcement Learning. LÄS MER

  3. 2. Random Edge is not faster than Random Facet on Linear Programs

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Nicole Hedblom; [2023]
    Nyckelord :Simplex method; simplex; Random Edge; Linear Programming; Random Facet; randomized pivoting rule; Markov decision process; Simplexmetoden; Random Edge; linjärprogrammering; Random Facet; Markov-beslutsprocess;

    Sammanfattning : A Linear Program is a problem where the goal is to maximize a linear function subject to a set of linear inequalities. Geometrically, this can be rephrased as finding the highest point on a polyhedron. The Simplex method is a commonly used algorithm to solve Linear Programs. LÄS MER

  4. 3. Risk-aware Autonomous Driving Using POMDPs and Responsibility-Sensitive Safety

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

    Författare :Caroline Skoglund; [2021]
    Nyckelord :Computer Science - Robotics; Autonomous driving; partially observable Markov decision process; motion planning under uncertainty; risk estimation;

    Sammanfattning : Autonomous vehicles promise to play an important role aiming at increased efficiency and safety in road transportation. Although we have seen several examples of autonomous vehicles out on the road over the past years, how to ensure the safety of autonomous vehicle in the uncertain and dynamic environment is still a challenging problem. LÄS MER

  5. 4. 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

  7. 5. A study of the exploration/exploitation trade-off in reinforcement learning : Applied to autonomous driving

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

    Författare :Ruwaid Louis; David Yu; [2019]
    Nyckelord :;

    Sammanfattning : A world initiative was set in motion for decreasing the amount of traffic accidents. Autonomous driving is a field which contributes to the initiative. Following report examines exploration/exploitationtrade-off in reinforcement learning applied to decision making in autonomous driving. LÄS MER