Contents
CartPole and Snake got you comfortable with the DQN loop, but Atari is where the training wheels come off. Instead of a handful of clean numbers describing the state, your agent now has to figure out what's happening from raw pixels — the exact same challenge DeepMind's original 2015 DQN paper set out to solve, and honestly the moment reinforcement learning stopped being a toy field and started being taken seriously.
I'll be upfront about something most tutorials gloss over: this project is a genuine jump in complexity and compute, not just a bigger version of Snake. Ever wondered why so many "train an AI to play Atari" tutorials quietly skip the part where training takes hours instead of minutes? That's exactly what we're not skipping today.
By the end of this guide, you'll understand the specific techniques that make learning from raw pixels actually work, and you'll have a working pipeline training on a real Atari game. IMO, watching your agent go from randomly flailing paddle movements to actually returning volleys in Pong is one of the most genuinely impressive moments in beginner RL :)
Why Atari Is a Fundamentally Different Problem
CartPole's state was four numbers. Snake's was a handcrafted 11-value representation. Atari hands you a raw game screen — a grid of pixels — and nothing else. That single difference changes almost everything about the approach.
The state is now an image, not a clean numeric vector, so a simple fully-connected network isn't going to cut it — you need convolutional layers to actually extract useful visual features. A single frame doesn't tell you about motion. Is that ball moving left or right? One frame can't answer that; you need multiple consecutive frames stacked together. Training takes real time. This isn't a five-minute laptop experiment anymore — expect training runs measured in hours, not minutes.
If you're coming from our earlier tutorials, our CartPole guide and Snake tutorial cover the DQN fundamentals — replay buffer, epsilon-greedy exploration, and the learning loop — that this project builds directly on.
Figure 1: From CartPole to Atari — scaling DQN to learn directly from raw pixel observations
Setting Up Your Environment
Atari environments now live in the Arcade Learning Environment (ALE), accessed through Gymnasium rather than the deprecated original Gym package.
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install "gymnasium[atari,accept-rom-license]" torch numpy stable-baselines3
That accept-rom-license extra is required — Atari ROMs come with a specific licensing agreement Gymnasium needs you to explicitly accept before it'll download them. Skip this and your environment creation call will just fail with a confusing error about missing ROMs.
Choosing Your First Game
import gymnasium as gym
env = gym.make("ALE/Pong-v5", render_mode="rgb_array")
print("Observation space:", env.observation_space)
print("Action space:", env.action_space)
Pong is genuinely the best starting point, not Breakout or Space Invaders. It has a small action space, simple visual dynamics, and a clear win condition — the same reasons CartPole works well as a first RL project, just one tier up in complexity.
The Preprocessing Pipeline (Where Most of the Magic Actually Happens)
Here's something worth internalizing early: raw Atari frames are wildly inefficient to learn from directly. A stack of preprocessing steps, established in the original DeepMind paper, makes the problem tractable.
Grayscale Conversion and Resizing
import cv2
import numpy as np
def preprocess_frame(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
resized = cv2.resize(gray, (84, 84), interpolation=cv2.INTER_AREA)
return resized
Color genuinely doesn't matter for most Atari games — converting to grayscale cuts your input size by two-thirds with essentially no information loss. Resizing down to 84×84 further shrinks the network's workload without meaningfully hurting what the agent can perceive.
Frame Stacking: Solving the Motion Problem
A single static frame can't tell your agent whether the ball is approaching or receding. The fix: stack four consecutive frames together as the actual input.
from collections import deque
class FrameStacker:
def __init__(self, stack_size=4):
self.stack_size = stack_size
self.frames = deque(maxlen=stack_size)
def reset(self, frame):
processed = preprocess_frame(frame)
self.frames = deque([processed] * self.stack_size, maxlen=self.stack_size)
return np.stack(self.frames, axis=0)
def step(self, frame):
processed = preprocess_frame(frame)
self.frames.append(processed)
return np.stack(self.frames, axis=0)
With four stacked frames, the network can infer velocity and direction just from comparing positions across the stack — motion becomes visible even though each individual frame is completely static.
Frame Skipping
Consecutive Atari frames barely differ from each other, so most implementations only make a decision every 4th frame, repeating the chosen action across the skipped ones. This dramatically speeds up training without meaningfully hurting decision quality, since nothing important usually changes frame-to-frame anyway.
Building the Convolutional DQN
Now the network itself needs to actually handle image input, unlike the simple linear layers that worked for CartPole and Snake.
import torch
import torch.nn as nn
class AtariDQN(nn.Module):
def __init__(self, num_actions):
super().__init__()
self.conv = nn.Sequential(
nn.Conv2d(4, 32, kernel_size=8, stride=4),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=4, stride=2),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1),
nn.ReLU()
)
self.fc = nn.Sequential(
nn.Linear(64 * 7 * 7, 512),
nn.ReLU(),
nn.Linear(512, num_actions)
)
def forward(self, x):
x = x / 255.0
x = self.conv(x)
x = x.view(x.size(0), -1)
return self.fc(x)
Notice the input channels are 4, not 3 — that's the stacked frames, not RGB color channels, feeding directly into the first convolutional layer. This exact architecture is essentially the one from the original DeepMind paper, and it's held up remarkably well as a starting point.
The Two Extra Tricks Atari Needs
CartPole and Snake got away with a basic replay buffer and epsilon-greedy exploration. Atari genuinely needs two more stabilization techniques on top of those.
Target Networks
policy_net = AtariDQN(num_actions)
target_net = AtariDQN(num_actions)
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
Here's the problem this solves: if you compute your target Q-values using the same network you're actively updating, you're chasing a constantly moving target — training becomes genuinely unstable. A separate target network, updated only periodically (say, every few thousand steps) rather than every single step, keeps the learning targets stable long enough for the policy network to actually converge.
A Much Larger Replay Buffer
from collections import deque
import random
replay_buffer = deque(maxlen=100_000)
Snake's replay buffer topped out around 100,000 experiences and that was plenty. Atari benefits from buffers of similar or larger size specifically because visual observations vary enormously — a small buffer risks overfitting to whatever recent gameplay happened to look like, rather than learning generalizable strategy.
A Genuinely Important Reality Check on Training Time
I want to be straight with you here, because plenty of tutorials aren't: this is not a five-minute training run. Training a DQN agent to competently play Breakout has taken reported setups around 6 hours on an RTX 4070 for 5 million training steps, even using lightweight hyperparameters. Some historical Pong implementations reportedly took over 10 million episodes to reach near-perfect play using less optimized approaches.
16+ GB of RAM and a GPU with 2+ GB of VRAM is a reasonable minimum hardware bar for this project. Don't expect visible competence in the first hour of training, even on a good GPU — early progress on Atari is much slower to show up than it was on Snake. Using Stable-Baselines3 instead of hand-rolling everything genuinely saves real debugging time here, since the preprocessing wrappers and stable training loop are already implemented and tested.
For GPU recommendations if you plan to train at scale, our best GPUs for deep learning guide covers what actually matters for RL workloads specifically.
The Easier Path: Stable-Baselines3
Given how many moving pieces this project has, using a maintained library instead of hand-rolling every component is a completely reasonable choice, not a cop-out.
from stable_baselines3 import DQN
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack
env = make_atari_env("ALE/Pong-v5", n_envs=4, seed=0)
env = VecFrameStack(env, n_stack=4)
model = DQN(
"CnnPolicy",
env,
buffer_size=100_000,
learning_starts=10_000,
verbose=1
)
model.learn(total_timesteps=1_000_000)
model.save("pong_dqn")
Notice how much of the manual pipeline — frame preprocessing, frame stacking, convolutional architecture, target networks — gets handled automatically here. I'd genuinely recommend starting with this version first, then going back to the from-scratch implementation once you understand what each wrapper is actually doing underneath.
For a deeper understanding of how DQN compares to other algorithms, our evaluating reinforcement learning algorithms guide covers the metrics and methods used to assess algorithm performance.
Common Mistakes People Make
I've hit a few of these myself, and seen the rest repeated across community implementations.
Skipping frame stacking entirely. A single static frame genuinely cannot convey motion — your agent will struggle to learn anything meaningful about moving objects without it. Using a target network update frequency that's too aggressive. Updating the target network too often reintroduces the instability it exists specifically to prevent. Expecting Snake-level training speed. Atari genuinely takes hours to days depending on the game and hardware — judging progress against Snake's timeline leads to premature, incorrect conclusions. Choosing a visually complex game first. Space Invaders or Ms. Pac-Man have far more visual complexity than Pong or Breakout — start simple before tackling anything with lots of moving enemies.
Choosing Your Next Game After Pong
Once Pong trains successfully, a natural progression exists.
Breakout — introduces a slightly more complex reward structure and requires more precise paddle positioning than Pong. Space Invaders — adds multiple simultaneous threats and a genuinely harder strategic layer. Ms. Pac-Man — significantly more complex, with multiple enemies and pathing considerations; a good stretch goal once the fundamentals feel solid.
If you want to formalize your RL knowledge after this project, our best courses for RL in robotics and game AI covers Hugging Face's Deep RL Course, NVIDIA's Physical AI path, and Stanford CS234.
Wrapping This Up
Training an Atari-playing agent takes everything you learned from CartPole and Snake — replay buffers, epsilon-greedy exploration, target networks — and adds the two genuinely new pieces raw pixel input demands: convolutional feature extraction and frame stacking to capture motion. The core DQN loop underneath barely changes; what changes is what the network has to see and process.
Remember that this project takes real compute time, measured in hours rather than minutes, and don't be discouraged if visible progress takes longer to show up than it did with Snake. FYI, Stable-Baselines3 handles most of the fiddly preprocessing wrappers for you — starting there before hand-rolling everything yourself is a completely legitimate way to learn this :)
Now go pick Pong as your starting game specifically, resist the urge to jump straight to something visually flashier, and watch your agent's return rate slowly climb from "can't hit the ball" to "actually rallying." That gradual climb is genuinely the best part of this whole project.