Skip to content

omerbsezer/Qlearning_MountainCar

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 

Qlearning_MountainCar

"The mountain car problem is commonly applied because it requires a reinforcement learning agent to learn on two continuous variables: position and velocity. For any given state (position and velocity) of the car, the agent is given the possibility of driving left, driving right, or not using the engine at all. In the standard version of the problem, the agent receives a negative reward at every time step when the goal is not reached; the agent has no information about the goal until an initial success."

mountaincarproblem

QLearning Implementation Using Gym.

"QLearning is a model free reinforcement learning technique that can be used to find the optimal action selection policy using Q function without requiring a model of the environment. Q-learning eventually finds an optimal policy"

Refs: QLearning: https://en.wikipedia.org/wiki/Q-learning

Mountain Car Problem: https://en.wikipedia.org/wiki/Mountain_car_problem

Mountain Car Open AI Gym: https://gym.openai.com/envs/MountainCar-v0/

Mountain Car Gym Git: https://github.com/openai/gym/wiki/MountainCar-v0

Open AI Gym: https://gym.openai.com/docs/

More Ref: https://github.com/llSourcell/Q_Learning_Explained

About

Mountain Car problem solving using RL - QLearning with OpenAI Gym Framework

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages