Reinforcement Learning vs Deep Learning vs Supervised Learning: A comprehensive comparison

Table of contents

Introduction: Why knowing the difference matters



Supervised learning underpins many everyday AI systems (like image classifiers or spam filters) by learning from examples of correct outputs.
Deep learning (a subset of machine learning) has propelled breakthroughs in understanding images, speech, and language by using multi-layered neural networks.
Reinforcement learning (RL) powers decision-making agents (like game-playing AIs and robotics) that learn via trial and error and feedback from their environment.

Supervised learning basics

How it works: The model iteratively makes predictions on training data and adjusts its internal parameters to reduce the error between its predictions and the true labels. Over time, it learns the patterns that associate inputs with the correct outputs.
Tasks: Supervised learning excels at tasks like classification (e.g., determining if an email is spam or not) and regression (e.g., predicting house prices). Essentially, any problem where you have historical examples of inputs and their desired outputs can be tackled with supervised learning.
Data requirements: This approach needs lots of labeled data. Gathering and labeling data can be time-consuming and costly, but with enough quality data, supervised models can achieve high accuracy.
Examples: Common algorithms include linear regression, decision trees, and neural networks trained on labeled datasets. For instance, training a neural network on thousands of labeled cat vs. dog images so it can classify pet photos is a supervised learning task.

Deep learning basics

How it works: Deep learning models are composed of layers of artificial neurons. Data is fed into the input layer, passes through hidden layers of transformations, and produces an output (like a class label or a numeric prediction). During training (often done in a supervised manner), the model adjusts the weights of these connections to minimize error. The deep architecture enables learning of very complex functions that shallow models (or manual features) might miss.
Where it fits: Deep learning has become the go-to approach for tasks involving unstructured data – images, audio, text – where finding patterns is hard for traditional methods. It shines in computer vision, natural language processing, speech recognition, and more. For instance, models like Convolutional Neural Networks (CNNs) revolutionized image processing, and Transformers (like the model behind ChatGPT) revolutionized language tasks.
Data and compute needs: A hallmark of deep learning is the need for large datasets and significant computational power (GPUs/TPUs). Training deep networks from scratch can require millions of labeled examples and extensive computing resources. However, once trained, these models can achieve state-of-the-art performance, often surpassing human-level accuracy in certain tasks (e.g. image classification).
Example: A classic deep learning example is image classification. Feed a deep neural network millions of labeled images (cats, dogs, etc.), and it will learn to identify them by itself. As noted, even GPT-4 or ChatGPT’s underlying model is a deep learning model – it was trained on massive text data to learn language patterns.

Reinforcement learning basics

Agent, Environment, Actions, and States: The agent is the learner or decision-maker (a software “robot” in simulation, or a real robot). The environment is the world the agent interacts with (e.g., a game, a physical room, or a stock market simulation). At any time, the agent observes the current state of the environment (a representation of the situation) and chooses an action. The environment then transitions to a new state and gives the agent a reward (a numerical score) indicating the immediate benefit of that action.
Trial and error learning: Initially, the agent doesn’t know which actions are best. It tries different actions and observes results. Actions leading to high rewards will be reinforced (encouraged), while those leading to penalties or low rewards will be discouraged.
Delayed rewards & cumulative return: One challenge in RL is that an action’s benefit might not be immediately obvious – some actions yield long-term rewards. For example, a move in chess might not pay off until many turns later. The agent seeks to maximize cumulative reward (also called return), not just immediate rewards, which means it must learn to plan ahead and sometimes sacrifice short-term gains for bigger future rewards.
Learning goal: Instead of learning an input-output mapping as in supervised learning, an RL agent learns a policy: a mapping from states to the best action to take in each state. Alternatively, some algorithms learn a value function, which predicts future rewards from a state, and use that to inform decisions.
Common algorithms: There are many RL algorithms, like Q-learning, Deep Q-Networks (DQN), Policy Gradients, and more. They differ in how they explore and update their knowledge, but all adhere to the core idea of learning from interaction. For example, Q-learning learns a value for each state-action pair indicating how good that action is in that state; Policy Gradient methods directly adjust the policy (the decision strategy) to maximize rewards.

Game AI: RL has produced superhuman game players. AlphaGo by DeepMind learned to play Go (and beat world champions) through RL combined with deep neural nets. Atari game agents were trained via deep reinforcement learning to play classic video games directly from pixel inputs. OpenAI’s Dota 2 bot and AlphaStar for StarCraft are other landmark achievements of RL.
Robotics: Robots learn locomotion, manipulation, or navigation through RL. For instance, a robot dog might learn to walk by trial and error, rewarding forward motion without falling. In industrial settings, RL can optimize robotic arms for efficient assembly tasks.
Autonomous vehicles and control: Driving policies, drone flight, or resource allocation problems can use RL to find strategies that adapt to complex, dynamic environments.
Finance & operations research: RL is explored for stock trading agents that seek long-term profit, or for optimizing logistics and supply chain decisions where each choice affects future outcomes.

Reinforcement Learning vs. Supervised Learning

Learning Signal: Supervised learning learns from explicit correct outputs provided for each input. The “teacher” provides the right answer (label) during training. Reinforcement learning, in contrast, has no predefined correct answer for each step. Instead, the agent discovers good actions by reward/punishment feedback after the fact. There is no supervisor labeling each action; feedback is often delayed until a sequence of actions is completed.
Objective: Supervised learning aims to minimize prediction error (e.g., classify images correctly). It treats each example independently – the model doesn’t typically consider a sequence of predictions. Reinforcement learning aims to maximize cumulative reward, which inherently involves planning across a sequence of actions. Each action can influence future states and rewards, so the problem is sequential in nature.
Data Requirements: A supervised model needs a historical dataset with input-output pairs to learn from, and its performance is limited by the scope of that data. An RL agent does not require a pre-labeled dataset; it often generates its own experience data by interacting with the environment. This means RL can, in some cases, learn from scratch in simulations without big data upfront. However, RL may need many trial runs (experiments) in the environment to learn effectively, which is a data generation process in itself.
Examples of Feedback: In supervised learning, if you show the model an image of a cat labeled “cat”, and it predicts “dog”, you immediately correct it – this forms a direct error signal used to update the model. In RL, if an agent takes a suboptimal action, there’s no immediate “correct action” given – the agent might simply get a lower reward and has to infer that its action was bad. For instance, a robot might try a move that causes it to fall, receiving a negative reward (penalty); it then knows in hindsight that the action was poor.
When to use which: Use supervised learning when you have a well-defined prediction problem and labeled data (e.g., classify emails as spam/ham, predict tomorrow’s temperature from historical data). Use reinforcement learning when you need an agent to learn through interaction how to accomplish a goal, especially when feedback is gradual and the strategy matters (e.g., training a game AI or an adaptive control system). Notably, RL is often more challenging to implement and requires careful design of the environment and reward structure.

Reinforcement Learning vs. Deep Learning

Nature of Approach: Reinforcement learning is process-oriented – it’s about learning through interaction to achieve a goal. Deep learning is model-oriented – it’s about using multi-layer neural networks to learn representations of data. In other words, RL is how an agent learns (via rewards), whereas deep learning is what the agent (or any ML model) might use to learn complex patterns.
Dependency on Data: Deep learning typically requires large datasets for training (especially in supervised settings). You gather a big batch of data and train a neural network to find patterns. In reinforcement learning, the agent often learns by exploring its environment, so it doesn’t necessarily start with a massive labeled dataset. Instead, it generates data on the fly. That said, RL can be data-hungry in its own way – complex environments might require millions of interactions to learn good policies.
Problem Domains: Deep learning excels at perception tasks: vision, speech, language understanding, where the goal is to map an input to an output (e.g., caption an image, transcribe audio). Reinforcement learning excels at decision-making tasks: game playing, robotics, navigation, where the goal is to devise an optimal sequence of actions. There are tasks where either or both are involved – for example, an autonomous car uses deep learning to perceive road signs (supervised learning on images) and reinforcement learning or planning algorithms to make driving decisions.
Integration: They are not mutually exclusive. In fact, modern deep reinforcement learning is exactly the intersection: using deep neural networks within a reinforcement learning framework. A deep learning model can serve as the function approximator in RL (for mapping states to actions or to value estimates), which allows RL to handle high-dimensional inputs like images. On the flip side, you generally wouldn’t hear of “using reinforcement learning inside a deep learning model” because RL is a top-level training paradigm, but techniques like learning to learn or meta-learning sometimes blur those lines.
Learning Outcome: A trained deep learning model (for example, a convolutional neural network for image classification) yields a static function: it takes an image and outputs a label. A trained reinforcement learning agent yields a policy or controller that dictates behavior – it’s an ongoing decision-maker that maps states to actions. The RL agent is meant to operate in an environment continually, whereas a deep learning model usually produces an output and that’s it.

Deep Reinforcement Learning vs. Deep Learning

Deep Reinforcement Learning (DRL): This is a subfield of ML where reinforcement learning algorithms are powered by deep neural networks. The “deep” in DRL refers to the use of deep learning models (often for approximating value functions or policies in RL). The advantage is that DRL can handle complex, high-dimensional state spaces – for example, raw pixel inputs from a game screen or continuous sensor streams in robotics – which classical RL struggled with. DRL agents can directly take in an environment’s raw observations (like images) and decide on actions, because the neural network can interpret those observations to estimate what’s a good action.
Traditional Deep Learning (without RL): Refers to using deep neural networks for tasks like classification, regression, or generative modeling in a supervised or unsupervised manner. The model learns to produce correct outputs for given inputs, but it does not make sequential decisions in an environment. For example, a deep learning model might translate a sentence from English to French – it learns this mapping from many example translations, but it’s not acting in an environment for a reward; it’s simply trained to output a correct translation.
Goal and Evaluation: In deep reinforcement learning, success is measured by how well the agent maximizes rewards in its environment (e.g., game score, task completion). Training is often trickier – one must ensure the agent explores enough and the reward signal leads to desired behavior. In deep learning, success is measured by accuracy or error rate on a given task (e.g., classification accuracy on a test set). Training involves minimizing a loss function via gradient descent on lots of data, which is more straightforward to evaluate.
Use Cases of Deep RL: DRL has led to some remarkable real-world (and simulated) results that plain deep learning can’t achieve alone, because they involve learning behaviors. Some examples:
Game AIs: DeepMind’s AlphaGo and AlphaZero used deep RL to master Go, chess, and shogi. Deep Q-Networks (DQN) kicked off the deep RL boom by learning to play Atari 2600 games from pixels, achieving human-level performance on many. These were victories of combining deep neural networks with the trial-and-error learning of RL.
Robotics & Control: DRL is used for end-to-end training of robotic control policies – for example, controlling a robotic arm directly from camera images, or teaching a drone to perform acrobatics. The “deep” component helps interpret camera images or complex sensor inputs, while the RL component figures out the control strategy. This enabled robots to learn tasks like grasping objects by themselves through simulation and real-world trials.
Autonomous Vehicles: Some aspects of self-driving, like decision-making (when to change lanes, how to navigate an intersection) have been explored with deep RL, where the car’s state (from sensors) goes into a neural network that outputs a driving action. The reward can be defined by progress along a route and safety (negative reward for collisions, etc.).
NLP and Dialogue Systems: While supervised deep learning is standard in NLP, researchers also experiment with RL for language – e.g., a chatbot that gets a reward for successful interactions. Deep RL can optimize policies in text generation or dialogue, where the “reward” might be user satisfaction or achieving a goal in conversation.
Use Cases of Deep Learning (Non-RL): Standard deep learning remains king for things like image and speech recognition. For example, diagnosing diseases from medical images with a deep network, or transcribing speech to text with a deep acoustic model – these don’t involve an agent taking actions for reward; they’re about making a single prediction accurately.

Deep Reinforcement Learning: What it is and why it’s oowerful

The network can serve as a policy function, directly mapping states to the best action probabilities (as in Deep Policy Networks or actor-critic methods).
Or it can serve as a value function (Q-function) approximator, estimating how good a given state (or state-action pair) is (as in Deep Q-Networks).

It eliminates manual feature engineering in complex environments. The neural net can learn to perceive important features (like opponent positions in a game, or obstacles in a robot’s vision) as part of learning the policy.
It enables RL to be applied to previously intractable problems. Classic RL struggled with large state spaces. Deep RL agents have achieved superhuman performance in domains with enormous state spaces (like Go, with more possible states than atoms in the universe).
DRL can handle continuous action spaces (e.g., steering angles) through approaches like Deep Deterministic Policy Gradient (DDPG) and others that leverage neural nets for approximation.
It leverages the massive advances in deep learning tooling and hardware. Libraries like TensorFlow/PyTorch and GPU acceleration help in training deep RL models efficiently. Techniques like experience replay and target networks stabilize training, making deep RL more feasible.

Recommendation Systems: Deep RL is being explored to build systems that can adapt to user behavior over time (for example, news or video recommendations where the system’s choices influence what the user will click on next, an interactive loop). The agent observes user interactions (state), recommends content (action), and gets reward based on engagement.
Industrial Automation: In manufacturing or operations, an AI agent might use DRL to optimize processes, like dynamically adjusting parameters on an assembly line for maximal efficiency or minimal waste, where direct supervised labels aren’t available but a reward (throughput, cost) can be measured.
Healthcare: Research has looked at applying DRL to treatment planning – an agent suggests treatment decisions (actions) over time, and the reward could be patient health outcomes. This is an ongoing research area, with potential to personalize and optimize healthcare decisions in complex scenarios.

Hands-on tutorial: Deep reinforcement learning with Python

Installation and setup

!pip install stable-baselines3 gym wandb

import gym
from stable_baselines3 import PPO  # using PPO algorithm (you can use DQN as well)
from wandb.integration.sb3 import WandbCallback
import wandb

# Configure and initialize W&B
wandb.init(project="deep-rl-cartpole", name="PPO_CartPole_example", config={
    "env": "CartPole-v1",
    "algorithm": "PPO",
    "total_timesteps": 50000
})

Environment and model setup

# Create the CartPole environment
env = gym.make("CartPole-v1")

# Initialize the PPO agent with a MLP policy (neural network) 
model = PPO(policy="MlpPolicy", env=env, verbose=1)

gym.make("CartPole-v1") creates the environment where each step the agent will get an observation (state) and reward.
PPO(policy="MlpPolicy", env=env) sets up a PPO agent with a default multi-layer perceptron policy network to decide actions. PPO (Proximal Policy Optimization) is a popular policy-gradient deep RL algorithm that is stable and versatile for continuous learning tasks.

Training the agent

# Train the agent for defined number of timesteps
model.learn(total_timesteps=50000, callback=WandbCallback())

Evaluating the results

obs = env.reset()
for step in range(1000):
    action, _states = model.predict(obs, deterministic=True)  # choose action
    obs, reward, done, info = env.step(action)  # take action in the environment
    env.render()  # render the environment (shows the cart-pole animation)
    if done:
        break
env.close()
wandb.finish()

What did we learn?

We used a deep learning model (neural network) as part of an RL algorithm (PPO) to learn a control task.
The agent learned by trial and error (many episodes of CartPole) and improved its policy.
We logged the process with W&B to visualize performance, highlighting how such tools can help in ML experimentation.

Conclusion

Supervised learning learns from labeled examples to make predictions on new data. It’s great for tasks where historical data with answers is available (classification, regression).
Deep learning is a powerful technique (often used within supervised learning) that uses neural networks with many layers to automatically learn representations from large datasets. It has driven recent AI advances in vision, speech, and language by handling complex data.
Reinforcement learning is a paradigm where an agent learns to make decisions through rewards and penalties by interacting with an environment. It’s the go-to approach for problems requiring a sequence of decisions (robotics, games, etc.) and doesn’t need labeled outputs – only a reward signal to optimize.
When looking at reinforcement learning vs deep learning, remember that RL is about learning to act, while deep learning is about pattern recognition using neural nets. They can and do work together: many RL successes use deep learning to handle rich inputs (this is deep reinforcement learning).
Deep reinforcement learning (DRL) combines the best of both worlds – the decision-making prowess of RL with the function approximation power of deep neural networks. DRL has achieved feats like mastering Atari games from raw pixels and beating human champions in strategic games, and it’s being applied in various domains from robotics to recommendations.
We walked through a simple DRL example with CartPole, showing how to train an agent using Python and track the training with Weights & Biases. This demonstrated in practice how an RL agent learns and how deep learning plays a role (the agent’s brain was a neural network).

References

Reinforcement Learning Explained: Overview, Comparisons and Applications in Business
Weights & Biases – Documentation on RL integration 
Reinforcement Learning Explained: Overview, Comparisons and Applications in Business
Deep-Learning vs Reinforcement Learning in AI - PC Guide