Introduction to Reinforcement Learning - Cartpole DQN

In order to achieve the desired behavior of an agent that learns from its mistakes and improves its performance, we need to get more familiar with the concept of Reinforcement Learning (RL).

RL is a type of machine learning that allows us to create AI agents that learn from the environment by interacting with it in order to maximize its cumulative reward. The same way how we learn to ride a bicycle, AI learns it by trial and error, agents in RL algorithms are incentivized with punishments for bad actions and rewards for good ones.

After each action, the agent receives the feedback. The feedback consists of the reward and next state of the environment. The reward is usually defined by a human. If we use the analogy of the bicycle, we can define reward as the distance from the original starting point.

CartPole is one of the simplest environments in OpenAI gym (collection of environments to develop and test RL algorithms). Implementing such a self-learning system is easier than you may think. Let’s dive in!

Text version tutorial: https://pylessons.com/CartPole-reinforcement-learning/
Full video playlist: https://www.youtube.com/playlist?list=PLbMO9c_jUD47r9QZKpLn5CY_Mt-NFY8cC
GitHub code: https://github.com/pythonlessons/Reinforcement_Learning/tree/master/01_CartPole-reinforcement-learning

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