Update README.md

README.md

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   # **Reinforce** Agent playing **CartPole-v1**
   This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
   To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
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+  **Policy-based learning** is directly approximating π without having to learn a value function- Our objective then is to maximize the performance of the parameterized policy using gradient ascent.
+  TL;DR: Having the cart learn to balance the pole via optimizing π for the best output; *the pole not falling over*
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+  This model only had 500 training timesteps- the average is 1000, which is the reason why the cart struggles so much with balancing the pole in the video; it has not trained enough for it.
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