In this we are training a deep learning algorithm to govern the actions of our RL agent, that is trying to find the optimal way to achieve a goal. The goal of training an RL agent is to determine the best next step to take to earn the greatest final return. We focus on building a reinforcement learning algorithm to master the cartpole environment.(Balance a pole, protruding from a cart, in an upright position by only moving the base left or right. Environment with a low-dimensional observation space.)
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