Simple q-learning implementation for taxi-v3 environment of Open AI gym.
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Updated
Feb 16, 2022 - Python
Simple q-learning implementation for taxi-v3 environment of Open AI gym.
In this paper we re-define MAXQ and the taxi environment and Implement them in R. We then apply Qlearning to the same problem. Our conclusion is that MAXQ works as good as Qlearning for this problem. Our aim is illustrate the advantages of using hierarchical reinforcement learning methods.
In this project, we tried two different Learning Algorithms for Hierarchical RL on the Taxi-v3 environment from OpenAI gym. SMDP Q-Learning and Intra Option Q-Learning and contrasted them with two other methods that involve hardcoding based on human understanding. We conclude that the solutions learnt by machine are way superior than humans for …
A simple Q-learning implementation in OpenAI Gym's "Taxi-v3" environment
Going through the Hugging Face Deep Reinforcement Learning course.
Implementation of Q-learning Algorithm on FrozenLake and Taxi environments
OpenAI's Taxi-v3 environment.
A Reinforcement Learning project solving the Gymnasium Taxi-v3 environment. It implements and compares tabular Q-Learning with a Deep Q-Network (DQN). The agent is trained to efficiently pick up and drop off a passenger in the fewest possible moves.
Epitech Project - Reinforcement learning agents solving the Taxi-v3 environment from Gymnasium.
Taksi-Yolcu problemi için Pekiştirmeli Öğrenme (RL) Ajanı
OpenAI Gym (Q-learning)
Q_LEARNING_EG_SARSA_TAXI
My solution to the OpenAI Taxi-v3 task
Finding the optimal route that a taxi has to take to pick up and drop a customer
Comparing SARSA, Q-Learning, and Double Q-Learning on Cliff Walking and Taxi-V3 Tasks
🚕 This repository contains the implementation of a Reinforcement Learning agent capable of solving the "Taxi" problem from the OpenAI Gym environment.
Scripts to test the influence of learning rate and discount rate on q-learning algorithms using OpenAI Gym's Taxi environment
This project simulates an intelligent taxi system using Q-learning in the Taxi-v3 environment, enhanced with traffic delays, weather penalties, and energy constraints. It demonstrates how reinforcement learning can adapt to real-world challenges while visualizing taxi rides for better understanding.
Compare q-learning, SARSA and Expected SARSA to solve AI gym's Taxi-v3 environment
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