Deep Q-Learning - My First Real Baselines Month

April was the warning label.

The moment I replaced a Q-table with a function approximator, RL stopped behaving like a tidy algorithm and started behaving like a fragile system.

So in May I did the obvious thing:

I leaned into it.

This is the month I finally used OpenAI Baselines in anger — not as a library to admire, but as a machine to operate, instrument, and debug.

And I picked the most classic deep RL entry point possible:

Deep Q-Learning (DQN).

Not because it’s the easiest.

Because it’s the first one where I can look at a training run and ask:

“Is this actually learning… or is it just spinning?”

Why DQN Is the Perfect First Deep RL System

DQN is conceptually satisfying:

• it’s still value-based, like tabular Q-learning
• it still tries to answer: “how good is this action in this state?”
• it uses a neural network as the Q-function approximator
• and it learns from experience

So in my head, it’s a clean continuation of March + April:

• March: Q-values in a table
• April: Q-values as a model
• May: Q-values as a neural network trained from experience

But in practice?

DQN is where RL starts to feel like engineering a moving machine.

Because DQN isn’t “one idea.”

It’s a bundle of ideas whose job is to prevent the system from blowing up.

DQN Felt Like a Bundle of Safety Systems (Not Just an Algorithm)

When people explain DQN casually, it sounds like:

“Just use a neural net to approximate Q-values.”

But Baselines DQN taught me the real story:

DQN is a stack of stabilizers.

Here are the ones that stood out immediately:

• experience replay (break correlation)
• target network (stop chasing a moving target too aggressively)
• epsilon-greedy exploration (avoid premature lock-in)
• reward clipping / normalization (keep learning signals sane)
• frame preprocessing (reduce the input chaos)
• delayed updates / warm-up steps (don’t learn from garbage early)

This is where my 2017 mindset came back full force:

Training isn’t “running an algorithm.”
Training is building a stable feedback system.

The Environments I Used to Stay Sane

I didn’t start with Atari.

Atari is where you go to feel humbled.

I started with environments that let me isolate failure modes.

My progression looked like this:

• CartPole (fast feedback, simple dynamics)
• MountainCar (reward is sparse enough to expose exploration issues)
• Acrobot (harder control, still manageable)
• then finally: a first Atari run (mostly to feel the scale of the problem)

What surprised me is how quickly “simple” environments still break when the training loop is misconfigured.

Deep RL can fail quietly even when the task is easy.

What I Watched Like a Hawk

In supervised learning, I watch:

• training loss
• validation loss
• accuracy

In DQN, that mindset is necessary but not sufficient.

The first week of May I basically stared at reward curves and got fooled repeatedly.

So I built a more RL-specific mental dashboard.

Here’s what I learned to pay attention to:

The First Deep RL “Failure That Felt Real”

In tabular RL, failure is usually obvious:

• value table doesn’t converge
• policy is wrong
• exploration is insufficient

In DQN, I hit a failure mode that felt different:

Everything looked like it was working.

The run produced numbers. The plots moved.

But the agent’s behavior didn’t actually improve in a way that made sense.

It was the first time I felt:

The system can generate convincing training noise that looks like progress.

That’s the psychological trap of deep RL.

So I started validating in a very blunt way:

• watch short rollouts
• check whether behavior changes match the reward curve
• run evaluation episodes with exploration minimized
• compare to random policy baselines

If the “learned” agent didn’t beat random clearly, I assumed I was hallucinating progress.

What “Debugging” Looked Like This Month

Debugging in DQN felt less like “fixing code” and more like “diagnosing dynamics.”

These were my recurring moves:

This felt similar to debugging deep nets in 2017:

start with a known-good baseline and shrink the problem until the system behaves.

Common Failure Modes I Now Expect (DQN Edition)

Here’s the list I wrote by the end of May — the things I now assume will break before I assume “the algorithm doesn’t work”:

Field Notes (What Surprised Me)

1) DQN is less “one method” and more “a stability recipe”

Before Baselines, I thought DQN was an algorithm.

After Baselines, it felt like a system design pattern:

• break correlations
• slow the moving target
• control exploration
• tame reward scale
• don’t learn too early from junk

2) Reward is not a trustworthy metric early

This month reinforced February’s bandit lesson:

Early reward is noisy, and in deep RL it can be misleadingly noisy.

3) The value function can become pathological long before reward shows it

If you don’t watch Q-value scale and TD error stats, you can drive straight into a cliff with a “fine-looking” curve.

4) Baselines is a teacher… if you read its signals

The library exposes enough diagnostics to make the learning process inspectable. But you have to actually look.

What’s Next

May was my first month where deep RL felt tangible:

• I could run something real
• watch it learn (or not)
• and debug it like a system

But DQN has a limitation I can’t ignore:

It’s built for discrete actions.

And it leans heavily on value estimation, which can be brittle.

Next month I’m switching gears:

Policy gradients.

Not because they’re easier.

Because I want to learn the other half of deep RL:

• direct optimization of behavior
• stochastic policies
• and the first time “entropy” becomes a real debugging signal

If DQN taught me how value-based deep RL breaks…

June will teach me how policy-based deep RL breaks.

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