Skip to content
Cart Pole problem solving using RL - QLearning with OpenAI Gym Framework
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
QLearningCartPole.py
README.md

README.md

QLearning_CartPole

"A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. The pendulum starts upright, and the goal is to prevent it from falling over by increasing and reducing the cart's velocity."

cartpolegif

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." Q-learning is a specific TD (Temporal-difference) algorithm used to learn the Q-function. If there is no large scale problems, we can use look up table like in this problem.

CartPole Results:

cartpoleresults

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

Cart Pole Problem: https://en.wikipedia.org/wiki/Inverted_pendulum

Cart Pole Open AI Gym: https://github.com/openai/gym/wiki/CartPole-v0

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

More Ref: https://medium.com/@tuzzer/cart-pole-balancing-with-q-learning-b54c6068d947

You can’t perform that action at this time.