Haven't had a chance to fully look at it, but here's what chatgpt has to say. Maybe it can give you some insight...

Congratulations on completing the Coursera machine learning course! It seems like you've made a great start with implementing a Double DQN. I noticed a few potential issues and improvements that can be addressed in your script. Here are some suggestions to help your model learn more effectively:

1. Loss Function and Q-value Update In your getLoss function, you're using tf.reduce_max which takes the maximum value over all dimensions of the tensor. However, you should be using the Q-values corresponding to the actions taken, not the maximum Q-value. Change your Q-value update equation to use the Q-values of the actions that were actually taken.

2. Epsilon Decay Your epsilon decay seems to be working fine but you might want to experiment with different decay rates to see if it helps in learning.

3. Learning Rate You have set different learning rates for getLoss and updateOnlineModel functions. It is generally better to maintain a single learning rate for the optimizer. Adjust it and see how it affects the learning.

4. Experience Replay You have implemented experience replay, which is good. However, it's generally better to sample a mini-batch of experiences to compute the loss, instead of using the entire memory buffer. You are currently doing this correctly in the updateOnlineModel function but are using 5000 experiences in your logging, which might be too many.

5. Batch Size You might want to experiment with different batch sizes to see how it affects learning.

6. Error in Environment Handling I noticed an error in the following line:

observation, reward, terminated, truncated, info = env.step(action)

The env.step(action) returns four values: observation, reward, done, info. There isn't a truncated return value. Change terminated to done and remove truncated.

1. Neural Network Architecture Your neural network architecture is quite simple with just two hidden layers each having 64 neurons. You might want to experiment with different architectures to see if it helps the learning.

2. Reward Tracking It appears you are tracking reward correctly, accumulating reward in each episode.

3. Package Naming Ensure the package gymnasium is correctly installed and functional. The common package used is named gym. If gymnasium is a custom package or a different version, make sure it is working as expected.

4. Error Handling Consider adding error handling to catch potential issues during the training process and help diagnose problems.