This page was last edited on 02 March 2026
To teach a robot (or software) to do something, the classic option is to write thousands of lines of instructions like “if you see this, do this”. But this strategy works when everything is clear in the environment. In a chaotic world, it is used Deep Machine Learning strategies.
In robotics with AI, we talk about optimizing a decision function. Think of it like using an invisible joystick: the robot tries various moves, sees what happens, and adjusts its “brain” to get a better result next time. We think of this as a feedback loop system. We have an input (sensor data), a decision (action), and an outcome. If the outcome is bad, the decision process changes.
In Reinforcement Learning, choosing a strategy is what decides whether training feels smooth and productive or like a constant uphill struggle. There are a few areas where this choice really makes a difference.
Learning Strategies in Reinforcement Learning
The “right strategy” isn’t something universal. It depends on your problem, your resources, and what you’re actually trying to achieve. But getting this choice right is usually the step that separates smooth progress from endless frustration.
1. Classic Deep Reinforcement Learning
Think of a child learning to ride a bike without training wheels. At first, the child fall. Once, twice, three times. But each fall is information. It is feedback. It’s a kind of conversation with the bike: “Not like that, try something different.” After enough trial-and-error, balance becomes possible. It just works.
This is how an agent learns in Deep Reinforcement Learning (Deep RL). It makes decisions, receives consequences, and adjusts the internal rules that guide it. All with the goal of getting more “reward” in the long run.
The word “deep” is not a marketing campaign. It means that the agent uses neural networks, powerful enough to process complicated things: images, sounds, even realistic physics.
Why is it interesting? Because this approach allows the agent to discover strategies that no one has told it before. It’s not a simple algorithm that follows fixed rules. Is an explorer who learns the map by himself.
What you also need to know is that classic deep RL is very inefficient at first (it consumes a lot of time), but it can find solutions that people have never thought of.
Benefits:
- Allows full exploration of the environment.
- Agents discover optimal strategies even in unknown or complex settings.
2. Reinforcement Learning from Human Feedback (RLHF)
An agent does two things, and a human tells which one is better.
It is too slow to let an agent to learn from the “rewards” they receive from the environment. We can speed up the learning process by letting the agent to gets feedback from people.
It involves collaboration. People label behaviors: “this is better, this is less good.” From these preferences, the agent learns not just to achieve a goal, but also to get closer to what people consider desirable. The result? Faster training and a direction much closer to human intentions.
Think of a dance coach. If you learn on your own, you try out chaotic moves until you find something that looks good. But with a coach who tells you in real time, “relax your arms, look ahead,” your progress is much faster and your steps look exactly as they should.
Benefits:
- Speeds up learning in hard or unsafe environments.
- Helps the agent align better with human goals and expectations.
3. Imitation Learning
Imitation learning is suppose to learn an agent by copying someone who already knows the way. It watches an expert demonstrate and then replicates its actions. It’s like watching a driver, a robotic arm, or even another well-trained agent do it, and try to repeat it too.
The advantage is obvious. The training is skip the chaotic stage of trial-and-error. Instead of making hundreds of costly or dangerous mistakes, the agent starts right away with a solid foundation. This is extremely useful when free exploration would be risky (like in a real car on the road) or expensive (when every test means time and broken parts).
Imagine you want to cook a sophisticated dish. You can invent the recipe from scratch and burn the pan a few times, or you can sit next to a chef and copy the steps: “this is how I cut the vegetables, this is how I season, this is how I adjust the heat.” The result appears much faster and looks much better.
Benefits:
- Reduces the time needed to reach good performance.
- Useful when exploration is dangerous, expensive, or time-consuming.
4. Inverse Reinforcement Learning (IRL)
The heavy part isn’t training the agent, but telling it what you really want from it. Writing a perfect reward function can be nearly impossible. How do you describe “drive carefully” in a mathematical formula? Or “pick up the object carefully“?
Inverse Reinforcement Learning (IRL) approaches the problem from a different angle. Instead of directly telling the agent what reward to pursue, you let an expert observe it. From the agent’s behavior, the agent tries to infer what hidden goal guided those actions. Then, based on that goal, it forms its own rules and learns to reproduce similar behaviors.
Imagine a detective watching a person’s actions to discover their true intentions. They may not have access to the whole story, but from the gestures, from the steps taken, they can figure out what they’re after: “Aha, they want to get to the station before 5 o’clock.” The agent does the same: it analyzes and reconstructs the goal from the clues.
Benefits:
- Useful when the reward function is hard to define explicitly.
- Helps design agents that understand complex or subtle goals.
5. Offline Deep Reinforcement Learning (Batch RL)
Sometimes, direct exploration can be risky or too expensive. In Offline Reinforcement Learning, the agent does not “beat the odds” in real time. Instead, it learns from a fixed collection of already collected experiences. It does not interact live with the environment. It refines its strategy by analyzing what has already happened.
Think of a pilot who wants to fly a new plane. Instead of jumping straight into the cockpit and experimenting in the air, he studies hundreds of flight simulations. He analyzes every scenario, every past error, and forms a solid strategy before he reaches reality.
The advantage is clear. You get rid of unnecessary risks and can train agents in environments where real interaction would be limited, expensive, or even dangerous.
Benefits:
- Reduces the risks and costs associated with live exploration.
- Makes it possible to train agents in scenarios where real-world interaction is limited or expensive.
Offline RL is used in medicine and healthcare, where you cannot allow an agent to try random actions on real patients. But past clinical data can be used to train useful and safe policies.
Next Step
Understanding how agents learn is just the beginning.
To build real-world Deep Reinforcement Learning applications, you also need a strong foundation in the essential mathematical concepts that power every agent.
In the next section, I begin with the basics of data representation, vectors, and how they are used in model training.
Your First Step << Previous | Next >> Vectors