What is deep reinforcement learning?
Length:
3 min
Published:
June 9, 2026
What is deep reinforcement learning?
Deep reinforcement learning (deep RL) combines two ideas. Reinforcement learning teaches a model by trial and error: it acts, gets a reward or penalty, and adjusts to earn more reward over time. A deep neural network gives it the ability to handle complex, high-dimensional inputs, such as raw pixels from a game or sensor data from a robot. Together they let an agent learn good behaviour in environments far too rich for hand-written rules. Deep RL is what taught AI to beat humans at Go and to control robots.
In plain words
Imagine teaching a dog a trick with treats, but the dog also has eyes that take in a whole messy scene. Reinforcement learning is the treats and corrections. The deep network is the eyes and brain that make sense of what the dog is looking at, so it can connect a complicated situation to the right move.
When to use it
- Control and robotics. Teaching a robot arm to grasp objects or a drone to fly stably.
- Games and simulation. Mastering games or training agents in simulated environments before the real world.
- Sequential decisions. Problems where each action changes the situation and you optimise a long-term outcome, not a single answer.
Common pitfalls
- Hungry for data and compute. Deep RL often needs millions of trials, which is why most training happens in simulation.
- Unstable training. Small changes in the reward or settings can make learning collapse. It takes careful tuning.
- Reward hacking. The agent optimises exactly what you reward, not what you meant. A badly designed reward leads to clever but useless behaviour.
Related articles:
- What is reinforcement learning? - The trial-and-error foundation that deep RL builds on.
- What is deep learning? - Machine learning with neural networks of many layers.
- What is a neural network? - The structure that lets deep RL read complex inputs.
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