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Learn Reinforcement Learning

Agents, rewards and policies, from Q-tables to deep RL.

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Reinforcement learning is about an agent that learns by acting: it observes a state, takes an action, receives a reward and gradually improves its policy to collect more reward over time. Unlike supervised learning, nobody hands it the right answer, so it must balance exploring new actions against exploiting what already works. The field rests on a compact set of ideas, Markov decision processes, value functions and the Bellman equation, that lead to Q-learning, policy gradients and modern methods like PPO. Learnr builds a reinforcement learning course around your math and coding level, with short lessons that pair each idea with a small environment you can reason about.

How to learn Reinforcement Learning, step by step

Learn the vocabulary on a grid world. Draw a small grid with a goal, a pit and walls. Define states, actions, rewards and the discount factor, then compute returns for a few paths by hand. Every later algorithm is a way of estimating these numbers.
Work through the Bellman equation. Write the value of a state as its immediate reward plus the discounted value of where you land next. Run value iteration on your grid until the numbers stop changing, and read the optimal policy straight off the result.
Code tabular Q-learning yourself. Solve FrozenLake or CliffWalking in the Gymnasium library with a Q-table and epsilon-greedy exploration. Plot reward per episode and try different learning rates and epsilon schedules to feel how fragile training can be.
Move to function approximation. When states are too many for a table, a neural network estimates values instead. Study DQN and its tricks, experience replay and a target network, then train it on CartPole and watch for sudden collapses in performance.
Learn policy gradients and PPO. Understand REINFORCE as nudging the probability of actions that led to high return, then actor-critic methods that cut its variance. Use a maintained library such as Stable-Baselines3 to run PPO before writing your own.
Read the standard textbook alongside practice. Sutton and Barto, Reinforcement Learning: An Introduction, is the classic reference. Pair a chapter at a time with short Learnr review lessons so terms like on-policy, bootstrapping and advantage stay sharp.

What you'll learn

Frame a problem as a Markov decision process with states, actions and rewards
Compute value functions using the Bellman equation on small examples
Implement tabular Q-learning with epsilon-greedy exploration
Explain how DQN and policy gradient methods scale RL with neural networks
Diagnose reward hacking and unstable training in RL experiments

What's inside your Reinforcement Learning course

A sample path Learnr builds for Reinforcement Learning. Your real course adapts to your goal and level.

1. Agents, environments and rewards
2. Markov decision processes and returns
3. Value functions and the Bellman equation
4. Monte Carlo, temporal difference and Q-learning
5. Deep Q-networks and function approximation
6. Policy gradients, actor-critic and PPO

How long does it take to learn Reinforcement Learning?

The core ideas through tabular Q-learning take about 4 to 8 weeks at 5 hours a week if you already code in Python. Reaching comfortable use of deep RL methods like DQN and PPO usually takes 3 to 6 months, longer without prior deep learning experience.

What you need before you start

Python, basic probability and expected values, and some comfort with machine learning. For deep RL, know how neural networks train with gradient descent, ideally in PyTorch.

Common mistakes to avoid

Designing a reward the agent can exploit, such as rewarding speed so it learns to crash early. Watch rollouts, not just the reward curve.
Trusting a single training run. RL results swing widely between random seeds, so compare several seeds before concluding one setting is better.
Starting with Atari or robotics before solving small environments, which hides bugs inside hours of slow, noisy training.

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Reinforcement Learning questions, answered

How is reinforcement learning different from supervised learning?

Supervised learning trains on labeled examples of the right answer. Reinforcement learning only gets a reward signal after acting, often delayed, so the agent must discover good behavior through trial, error and exploration.

Where is reinforcement learning used?

Game-playing agents, robotics control, recommendation and resource scheduling are common examples. Variants of it are also used to fine-tune language models from human or automated preference feedback.

Do I need advanced math?

For the fundamentals, probability, expected values and some algebra are enough. Policy gradient derivations use calculus, and reading research papers needs more, but you can build working agents before mastering all of it.