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.
Experiment 1: Comparison of key bandit algorithms; Experiment 2: Comparison of Q and SARSA Learning on Taxiv3 environment' ; Experiment 3: Comparison of Q, SARSA and CEM Learning on LunarLanderv2 Environment
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.
OpenAI Gym (Q-learning)
Compare q-learning, SARSA and Expected SARSA to solve AI gym's Taxi-v3 environment
Implementation of Q-learning Algorithm on FrozenLake and Taxi environments
Q_LEARNING_EG_SARSA_TAXI
Reinforcement Learning practice for Taxi-v-3 and Frozen Lake environments
Scripts to test the influence of learning rate and discount rate on q-learning algorithms using OpenAI Gym's Taxi environment
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
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