Simple q-learning implementation for taxi-v3 environment of Open AI gym.

Taxi-v3 project using OpenAI 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.

A simple Q-learning implementation in OpenAI Gym's "Taxi-v3" environment

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 …

OpenAI's Taxi-v3 environment.

Compare q-learning, SARSA and Expected SARSA to solve AI gym's Taxi-v3 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.

OpenAI Gym (Q-learning)

Implementation of Q-learning Algorithm on FrozenLake and Taxi environments

Q_LEARNING_EG_SARSA_TAXI

My solution to the OpenAI Taxi-v3 task

Reinforcement Learning practice for Taxi-v-3 and Frozen Lake environments

Finding the optimal route that a taxi has to take to pick up and drop a customer

Scripts to test the influence of learning rate and discount rate on q-learning algorithms using OpenAI Gym's Taxi environment