Why are the rewards of my RL agent for the Atari Breakout game decreasing after a certain number of episodes? The agent is trying to master the Atari Breakout game.

Here is my code

Is that normal that reward_100 decreased that much after it hits 4.5? Is there a way to avoid that behavior?

Be aware that reward_100 is simply mean_reward = np.mean(self.total_rewards[-100:]). In other words, it is the mean over the last 100 rewards. On the graph, reward_100 represents de y-axis and th number of episodes the x-axis.
It seems that decaying the learning rate solved my problem. I changed learning_rate from 0.001 to 0.0001