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We will then proceed to make a simple hard-coded policy, make the agent with that policy, and examine the performance.
Then we train a neural network and observe the performance.
Later, we learn a concept named credit assignment problem, understand the policy gradients algorithm, and implement it to observe its effect in the improvement of the game.
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