Understanding LSTM Training and Validation Graph and their metrics (LSTM Keras)
From visually inspecting the graph, we see that the validation loss and accuracy has improved with each epoch - with the training loss and accuracy higher than that of validation. This indicates that accuracy has improved with training.
As suggested in another post, one potential solution is to calculate the exponential moving average of the validation loss to remove the oscillations and better determine the improvement in this metric.
If you are finding that the test accuracy is higher than that of training, this might suggest underfitting. This could imply that more training on your model is required, or has been over-regularized.