# Rai's ML mistakes, part 2 of ∞

Posted on 2021-01-04

Continuing my list of ML mistakes from last time, here’s:

# Rai’s ML mistake #4: Too much autograd

So here I am, writing an agent for LunarLander-v2. I’m using Q-learning. I approximate q*(s, a) as w(s, a) with a neural network, taking a vector representing the state, and outputting one approximate action value per output. The neural network is trained to minimize squared TD error on the policy the agent’s running, which is ε-greedy with respect to :
\begin{align*} \require{extpfeil} \ell(w) &= \mathop{\mathbb{E}}\limits_{(S \xrightarrow{A} R,S') \sim \mathrm{greedy}(\mathrm{\hat{q}}_w)} \left[ \left(\mathrm{\hat{q}}_w(S, A) - (R + \gamma \max_{A'} \mathrm{\hat{q}}_w(S',A')) \right)^2 \right] \\ \text{output } &\arg\min_w \ell(w) \end{align*}

## Not quite off-policy

One note about the “policity” of this method.

Tabular Q-learning without function approximation is off-policy - you learn about π* from experience (SAR, S′) sampled from any (sane™) policy. You just keep updating (S, A) towards R + γ ⋅ maxA(S′, A′), and to max  is there because you want to learn about the optimal policy.

But note that in ℓ(w), the experience (SAR, S′) is sampled from the policy greedy(w). We need to expect over a policy, because we’re using function approximation, so presumably we cannot learn a w which would make w exactly fit q*. So we have to pick out battles for how well do we approximate q* - we care about approximating it closely for states and actions actually visited by the estimation policy.

Instead of assuming that we can sample (SAR, S′) from greedy(w) (so that we can approximate the expected squared TD error over it), I guess you could use the general importance sampling recipe to get rid of that:
$$\mathop{\mathbb{E}}_\limits{X\sim \pi}[\mathrm{f}(X)] = \mathop{\mathbb{E}}_\limits{X\sim b}\left[\mathrm{f}(X) \cdot \frac{\pi(X)}{b(X)}\right]$$

So, we want to minimize .

Note that depends on w (via w) in 3 places:

1. In w(S, A), which we are trying to nudge to move to the right place,
2. in R + γmaxAw(S′, A′), which is a sample from a distribution centered on qgreedy(w)(S, A),
3. and in the distribution we’re taking the expectation on.

In practice, we hold (2) and (3) constant, and in one optimization step, we wiggle w only to move w(S, A) closer to targets. That means that in our gradient, we are ignoring the dependency of (2) and (3) on the w that we are optimizing, which makes this not a full gradient method, but instead a semi-gradient method.

## Experience replay

My first shot at this agent just learned from 1 step (sampled from ε-greedy policy for w) at a time. It worked in the sense that it ended up learning a policy close enough to “solving the environment”. (The environment says the “solved reward” is 200. I got maybe like 150-180 over 100 episodes, so not quite there, but it’s close enough for me to say “meh, I’ll wiggle a few hyperparameters and get there”.)

But to learn a fair policy, it took the agent about 10 000 episodes, and the per-episode total reward over time made a spiky ugly graph:

I don’t like that it takes all of 10 000 episodes, and I don’t like how spiky and ugly the chart is.

Experience replay means we store a bunch of experience (SAR, S′)1, 2, … in a buffer, and instead of updating w by some gradient-based optimization method (I used ADAM) to minimize squared TD error one step at a time, we update it to minimize squared TD error over the whole buffer, a bunch of steps at a time.

Experience replay should make learning more sample-efficient (so it should need less than 10 000 episodes). Also, it should reduce one source of “spikiness and ugliness” in the chart, because the chart will be doing step updates on a larger batch. Making the batch larger should reduce the variance of the updates.

## Broken code

So, here’s how I initially implemented one step of the update. self.experience_{prestates, actions, rewards, poststates, done} holds the experience buffer (S, A, R, S respectively for observed transition SAR, S, plus flag to signal end of episode).

@tf.function
def q_update(self):
# \max_{A'} \hat{q}(S', A')
best_next_action_value = tf.reduce_max(
self.q_net(self.experience_poststates), axis=1)
# If episode ends after this step, the environment will only give us
# one step of reward and nothing more. Otherwise, the value of the next
# state S' is best_next_action_value.
next_state_value = tf.where(
self.experience_done,
tf.zeros_like(best_next_action_value),
best_next_action_value)
targets = self.experience_rewards + self.discount_rate * next_state_value

# For all states S_i in the experience buffer, compute Q(S_i, *) for all
# actions.
next_action_values = self.q_net(self.experience_prestates)
# Select Q(S_i, A_i) where A_i corresponds to the recorded experience
# S_i --(A_i)--> R_i, S'_i, done_i.
indices = tf.stack(
(tf.range(self.experience_buffer_size), self.experience_actions),
axis=1)
values_of_selected_actions = tf.gather_nd(next_action_values, indices)

loss = tf.keras.losses.MeanSquaredError()(
values_of_selected_actions, targets)

self.optimizer.apply_gradients(zip(grad, self.q_net.trainable_variables))

What’s wrong here?

The symptom is that the policy is not improving. The total reward per episode is just oscillating.

## The problem

Remember how I said it’s a semi-gradient method?

Here’s the fix:

@tf.function
def q_update(self):
# \max_{A'} \hat{q}(S', A')
best_next_action_value = tf.reduce_max(
self.q_net(self.experience_poststates), axis=1)
# If episode ends after this step, the environment will only give us
# one step of reward and nothing more. Otherwise, the value of the next
# state S' is best_next_action_value.
next_state_value = tf.where(
self.experience_done,
tf.zeros_like(best_next_action_value),
best_next_action_value)
targets = self.experience_rewards + self.discount_rate * next_state_value

# For all states S_i in the experience buffer, compute Q(S_i, *) for all
# actions.
next_action_values = self.q_net(self.experience_prestates)
# Select Q(S_i, A_i) where A_i corresponds to the recorded experience
# S_i --(A_i)--> R_i, S'_i, done_i.
indices = tf.stack(
(tf.range(self.experience_buffer_size), self.experience_actions),
axis=1)
values_of_selected_actions = tf.gather_nd(next_action_values, indices)

loss = tf.keras.losses.MeanSquaredError()(
values_of_selected_actions, targets)

self.optimizer.apply_gradients(zip(grad, self.q_net.trainable_variables))

So, what was the problem?

The code calls the Q network twice: once to compute the targets (R + γ ⋅ maxAw(S′, A′)), once to compute w(S, A). Then, we will compute a loss, and we will take its partial “semi-derivative” with respect to w, and apply the gradient to bring w(S, A) closer to the target.

The problem was: I also put the target computation into GradientTape scope, so the optimization was given the freedom to change not just w(S, A), but also w(S′, A′). So the fix was just to move computing the targets out of the GradientTape scope.

I looked at this code basically non-stop for 2 hours, and I realized the error when I took a break and talked with a friend.

## Pet peeve #47: math typesetting

The full list of previous 46 pet peeves will be provided on request, subject to a reasonable processing fee.

### MathJax, \hat and \mathrm

is a function (of w, S, A), not a variable, so it shouldn’t be typeset in italic. I tried using \hat{\mathrm{q}}_w. I believe that works in LaTeX but turns out that MathJax is not willing to render it ($\hat{\mathrm{q}}$). But \mathrm{\hat{q}} is perfectly fine: . But \mathrm{\hat{q}}_w is perfectly fine: w.

### MathJax and inline \xrightarrow

Also, my MathJax doesn’t seem to understand \xrightarrow in inline equations. That’s a shame, because S \xrightarrow{A} R, S' is more readable than S \rightarrow_A R, S' (SAR, S), which I used here instead (in inline equations). It looks like this:
$$S \xrightarrow{A} R, S'$$
Let me know if you know what’s up with those MathJax things. I wonder if it’s MathJax being wrong, or me sucking at LaTeX.

### Why I’m a math typesetting snob

Typesetting things that aren’t variables as if they were variables really bugs me, because it makes the formulas really ugly. And the font you use to typeset a math thing is a very useful hint for the reader about what sort of object it is. I learned a bit about it when volunteering as a KSP organizer - KSP is full of math snobs. Compare:
\begin{align*} \mathrm{Loss}(w) = \sum_i (\mathrm{f}(x_i) - y_i)^2 \\ Loss(w) = \sum_i (f(x_i) - y_i)^2 \end{align*}
In the second one, it takes a bit of processing to understand that Loss is not a multiplication (L ⋅ o ⋅ s ⋅ s), and that f(xi) is function application.

If you want to read more, you can take a look at Typographical conventions in mathematical formulae on Wikipedia. Or maybe some LaTeX / TeX books or reference material might have a lot of explanations, like “use this in these situations”. And also good math books often have a large table at the front which explains used conventions, like “w is a vector, X is a matrix, f is a function, …”