Reinforcement Learning Application: CartPole Implementation Using QLearning

“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.”

Github Code: https://github.com/omerbsezer/QLearning_CartPole


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:


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


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