-
Notifications
You must be signed in to change notification settings - Fork 0
akar5h/Q-Learning-Reinforcement-Learning
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model (hence the connotation "model-free") of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds a policy that is optimal in the sense that it maximizes the expected value of the total reward over any and all successive steps, starting from the current state.[1] Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. Q names the function that returns the reward used to provide the reinforcement and can be said to stand for the quality of an action taken in a given state. This repository contains a python implimentation of Q-Learning algorithm in python to train a model on Frozen Lake Scenario , where the robot has to reach the destination from start in an n*n grid . The n*n grid also contains obstacles and reward .
About
No description, website, or topics provided.
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published