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Taxi Route Optimization with Reinforcement Learning

IntermediateSkill Level
4.8+
239 reviews
Updated 03/2025
Solve the Taxi-v3 environment using Q-learning, ensuring efficient AI-driven transportation.
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PythonMachine LearningArtificial Intelligence1 hr1 Task1,500 XP2,671

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Project Description

Taxi Route Optimization with Reinforcement Learning

Navigate the bustling streets of a virtual city as a taxi driver in this engaging reinforcement learning project. Utilize Q-learning to optimize your routes, ensuring passengers are efficiently picked up and dropped off. Train a reinforcement learning (RL) agent to solve the Taxi-v3 Gymnasium environment, ensuring optimal AI-driven transportation!

Taxi Route Optimization with Reinforcement Learning

Solve the Taxi-v3 environment using Q-learning, ensuring efficient AI-driven transportation.
Start Project
  • 1

    Task 1

Don’t just take our word for it

*4.8
from 239 reviews
88%
12%
0%
0%
0%
  • Albert
    5 hours ago

    good project

  • Yuan
    17 hours ago

    Overall a very good practice to review the Q learning. It would be better if the problem can clarify that the epsilon-greedy strategy is expected, rather than the vanilla version of Q learning.

  • Poonam
    2 days ago

  • Adam
    3 days ago

  • Emmanuel
    5 days ago

  • Elizaveta
    6 days ago

"good project"

Albert

"Overall a very good practice to review the Q learning. It would be better if the problem can clarify that the epsilon-greedy strategy is expected, rather than the vanilla version of Q learning."

Yuan

Poonam

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