<commit_before>In my first report I wrote like I was sure of what I'd found. In this report I
do the opposite: I ask questions about what I found.

- Is the Jensen-Shannon divergence useful in this case? What does it tell me? Is
  it sufficient to show that it goes down drastically? Is measuring it between
  the final histogram and all histograms good? Is it better to measure it
  between adjacent (in time) pairs of histograms?

- Should I use a logarithmic plot for Jensen-Shannon?

- The Kullback-Leibler divergence has a more clear and well-known
  interpretation, but can't be used without modification, because the histograms
  have zeroes. Is there any way to patch over this? Does it retain its
  interpretation after patching?

- Where do those sudden jumps in the histogram development plots come from? Why
  are they not visible in the histogram with 25 bins? They occur in Sarsa(λ),
  but even more astonishingly in Q-learning where one bin on the y-axis is blue
  (red), the next green, and the next blue (red) again.

- Why is the histogram for Q-learning quite stable for six million time steps
  and then starts changing again? This is even more crass when the data from
  time steps after crosses is left in.

- Why is the shape of the histogram for Q-learning more regular? Why is the
  shape of the histogram for Sarsa(λ) not regular? Actually it's quite regular
  except for the bars around and left of zero. Why does the histogram for
  Q-learning stabilize more quickly?

- Are the histograms and mean/std plots sufficient to show how interruptions
  influence the behaviour of Sarsa(λ) and Q-learning?

- How does the way I run the experiments and measure affect the apparence of
  influencedness? In other words, are Sarsa(λ) and Q-learning actually quite
  influence-resistent (interruptible), but I make them seem not so?

- Why do the graphs of the means for Q-learning look so similar?

- Why is the difference between the means (uninterrupted/interrupted) fairly
  similar to the difference between standard deviations?

- Why is the variance almost constant over time?<commit_msg>questions.md: Revise slightly<commit_after>In my first report I wrote as if I was sure of what I'd found. I even tried to
hide flaws. I have learned better. In this report I do the opposite: I ask
questions about what I found.

- Is the Jensen-Shannon divergence useful in this case? What does it tell me? Is
  it sufficient to show that it goes down drastically? Is measuring it between
  the final histogram and all histograms good? Is it better to measure it
  between adjacent (in time) pairs of histograms?

- Should I use a logarithmic plot for Jensen-Shannon? I'm using it now. Should I
  change back to linear?

- The Kullback-Leibler divergence has a more clear and well-known
  interpretation, but can't be used without modification, because the histograms
  have zeroes. Is there any way to patch over this? Does it retain its
  interpretation after patching?

- Where do those sudden jumps in the histogram development plots come from? Why
  are they not visible in the histogram with 25 bins? They occur in Sarsa(λ),
  but even more astonishingly in Q-learning where one bin on the y-axis is blue
  (red), the next green, and the next blue (red) again. (After changing the
  scale this has changed to a less drastic yellow-green (blue-green) to green
  and back.)

- Why is the histogram for Q-learning quite stable for six million time steps
  and then starts changing again? This is even more crass when the data from
  time steps after crosses is left in.

- Why is the shape of the histogram for Q-learning more regular? Why is the
  shape of the histogram for Sarsa(λ) not regular? Actually it's quite regular
  except for the bars around and left of zero. Why does the histogram for
  Q-learning stabilize more quickly?

- Are the histograms and mean/std plots sufficient for showing how interruptions
  influence the behaviour of Sarsa(λ) and Q-learning?

- How does the way I run the experiments and measure affect the apparence of
  influencedness? In other words, are Sarsa(λ) and Q-learning actually more
  influence-resistent (interruptible) than I make them seem?

- Why do the graphs of the means for Q-learning look so similar?

- Why is the difference between the means (uninterrupted/interrupted) fairly
  similar to the difference between standard deviations?

- Why is the variance almost constant over time?

When I ask these questions, I include programming and thinking errors of mine as
potential answers.
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