Reinforcement Learning Explained: The Complete Beginner’s Guide to How Machines Learn from Experiences

Reinforcement Learning (RL) is a very important type of Machine Learning(ML), alongside supervised learning and unsupervised learning.

Many people think that RL is just an algorithm, but RL is not a single algorithm. It is a method by which an agent learns from its own mistakes and successes, just like a child learns to ride a bike.

How does a machine (agent) learn from experience?

Just like a human:

• tries something,
• sees what happens (good or bad),
• and next time make a better decision.

This tutorial helps you understand everything that really matters:

• Intuition. The moment when RL becomes clear in your mind.
• Why robots need RL in the real world. Because the world is unpredictable, you can’t write rules for every situation.
• The simple theory behind RL. No heavy formulas. It is a system for making decisions over time and can be described by eight fundamental questions:
  • who is -> the agent,
  • what does it see -> the state,
  • what can it do -> the action,
  • why is it doing this -> the reward,
  • how does it decide -> what is the policy,
  • how much is this worth -> the value,
  • how does it evaluate the final result -> what is the return,
  • how does it learn new things -> what is exploration.
• An example of an RL agent for a 2WD robot. You will see how the robot transforms distance and signals from sensors into intelligent decisions.
• Mistakes that ruin an RL project.

If RL has ever seemed confusing to you, this guide will make it clear. It is no longer something abstract. It becomes something logical and easy to understand.

TABLE OF CONTENTS

In this tutorial, I’ll cover the following subjects:

• The Moment Reinforcement Learning Finally Makes Sense
• Why Robots Need Reinforcement Learning to Survive the Real World
• The Simple Theory Behind How Machines Learn from Experience
• A Robot Example: From Raw Sensors to Smart Decisions
• The Hidden Mistakes That Break RL Projects
• Reinforcement Learning Is Not an Algorithm, It’s a Relationship

The Moment Reinforcement Learning Finally Makes Sense

Reinforcement Learning starts to make sense the moment you stop thinking of it as an algorithm and start seeing it as a process.

Here you should be understood that:

• RL is a loop between agent and environment,
• agent decides, environment responds, agent learns,
• this is repeated until optimal behavior emerges.

It is like training a dog, not solving an equation.

RL is the type of AI that learns from interaction, not from labels. In supervised learning, you know the correct answer. In RL you don’t know the correct answer. The agent only receives a reward, sometimes rare and noisy. It has to discover for itself “what it had to do to be good.”

RL means “what action is worth it,” not “what action is right.” All actions are allowed.

There is no teacher. There is no “right vs. wrong.”

There is only:

• ”what helped me in the long run?”
• “what got me into trouble?”

This is the key point that makes RL different from anything else in ML.

In conclusion, an RL agent is like a person who tries something and sees what the consequences are. The agent’s goal is to maximize the long-term benefits.

• Policy = the agent’s strategy for making decisions.
• Reward = the feedback received after each action.
• RL learns everything through trial-and-error.

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Why Robots Need Reinforcement Learning to Survive the Real World

If you take a robot out of a simulation and put it in a real environment, with mud, wind, friction, unpredictable objects, and noisy sensors, a robot programmed only with rigid rules degrades rapidly in unstructured environments.

Survival for a robot means that it:

• does not crash,
• does not get stuck,
• does not tip over,
• does not get stuck in a situation that the programmer did not anticipate,
• continues to do its job under changing conditions.

Reinforcement Learning is the type of AI that can adaptively learn exactly from these situations.

1. The real world is chaotic, not deterministic

In simulations we have the same action with the same result.

In the real world the same action can have different results due to noise, friction, bumps, light, temperature, etc.

A robot must learn to function in permanent uncertainty. Classical programming does not cope here.

2. Manual programming (IF…THEN) cannot cover all situations

A robot in an orchard, shopping mall, a warehouse or on the asphalt encounters an infinite number of possible situations.

You can’t write “if you see this, do this” code for the whole universe. RL learns general strategies, not enumerations of cases.

3. Robots have imperfect sensors. Sensors have noise, offset, drift

A LiDAR, IMU or camera sensor:

• may have error,
• may “jump”,
• may return incorrect values,
• react differently in different weather.

A robot with RL learns to ignore or automatically compensate for these imperfections.

4. Robot dynamics change over time

• batteries discharge,
• the aging of an electric motor leads to performance decline and potential failure,
• wheels wear out,
• the weight of the robot can change.

A PID controller or a manual script cannot adapt as an RL agent adapts based on new experiences.

5. Real-world environments require continuous interaction

A robot must make decisions:

• every second,
• on the fly,
• as the environment changes.

RL is the type of AI with a sequential decision architecture, not just data analysis.

6. RL learns optimal behavior, not just valid behavior

A hand-programmed robot can work. A RL robot can be efficient, adaptive, robust, intelligent.

RL maximizes:

• safety,
• performance,
• low power consumption,
• durability,
• speed.

In conclusion, RL is not optional in robotics. It is inevitable.

Any robot that has to operate in an unstructured environment (orchard, field, road, house, real factory) will need:

• adaptation,
• controlled exploration,
• learning from consequences,
• strategy for the future.

Another aspect is that RL does not replace control, but amplifies it.

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The Simple Theory Behind How Machines Learn from Experience

If in the previous chapters you discovered intuition and why robots need RL, here you will learn how the learning process really works.

This chapter explains, without formulas and without hard math, the real mechanism by which an agent learns from its experiences.

1. Agent

The agent is the entity that makes decisions. It is the “doer.” Its role is to choose actions based on what it perceives. Without an agent, there is no RL.

1.1 The agent does not know the rules of the environment
It has no predefined instructions. It does not know the dynamics of the world it is in. It only learns from consequences, not from explicit knowledge.

The agent takes an action, then observes what comes next. The fundamental relationship is:

action –> consequence –> adjustment

It is a process of trial and error. The agent learns to improve its behavior over time:

• it does not become good instantly,
• behavior evolves gradually, through the accumulation of experience,
• each episode contributes to a better policy.

1.2 The agent has a goal defined by the reward
It does not set its own goals. The reward defines its goal. The agent only maximizes what the reward tells it has value.

1.3 The agent transforms experiences into strategy (policy)
The agent learns implicit rules, not explicit ones. From past experiences, it creates a stable way of making decisions.

1.4 The agent is adaptive, not rigid
It can adjust to changes in the environment. It learns from surprises, uncertainties, noise. Which is a major difference from classical programming.

1.5 The agent does not memorize actions, but learns a general behavior
Therefore it can generalize in new scenarios.

• it does not remember “if X → Y,”
• it learns a general way to react in various situations.

1.6 The agent makes sequential, not independent decisions
Each decision influences the next state. In RL, the past matters for the future. The agent learns chains of actions, not isolated actions.

1.7 The agent is responsible for the exploration/exploitation balance
It has to try new things to discover better solutions. It has to use what it has learned to be effective. It is the fundamental tension in RL.

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2. State

The robot sees the world through its “eyes“: sensors and cameras. But it does not see everything perfectly, the real world is sometimes blurry or confusing for it.

2.1 A state is the complete description of the situation in which the agent is, at a certain moment
In theory, states mean: everything the agent needs to know to make the best decision.

In other words:

• the robot’s position,
• how fast it is moving,
• where the obstacles are,
• what its objective is,
• the environmental conditions.

2.2 States are different from the entire reality in which the robot works
In the real world:

• you can’t observe everything,
• you don’t have access to perfect values,
• the real state of the universe is infinitely more complex,
• the robot only sees a small part of it.

2.3 State is an abstraction, not the complete reality
In RL, the agent rarely has access to the true underlying state of the environment.
In practice, the agent only observes partial, noisy, or delayed observations, which may not contain all the information required to make optimal decisions.

2.4 The state is the starting point of every decision
Without a state, there is no action, policy, reward, or learning.

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3. Observation

Observation is what the robot actually receives as input.

Observation is:

• raw signals from sensors,
• a partial and imperfect view of reality,
• what the agent “thinks” about the state it is in.

Important difference:

• STATE = complete reality.
• OBSERVATION = what the robot perceives.

3.1 A robot doesn’t see the “real position“
It sees:

• LiDAR distances = approximate distances, with noise;
• IMU readings = acceleration + rotation, with drift;
• Camera frames = incomplete images, with variable light;
• Ultrasonic sensors = values ​​that jump;
• Wheel encoders = number of ticks that can slip on mud;
• GPS = offset of a few meters in difficult areas;
• Proximity sensors = detect, but do not measure precisely;

3.2 Observation is what reaches the agent, not what is in the world

Observation is only an approximation of the real state. The reason is that the real environment is chaotic. Sensors are imperfect. Perception is limited by noise + latency + distortions.

And yet the agent has to make good decisions based on these incomplete signals.

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4. Action

An action is what the agent decides to do at that moment. It is its instantaneous choice, exactly at the moment it receives the observation from the sensors.

For a 2WD robot: the action can be “spin the wheels forward.“

For a robotic arm: the action could be “move the joint +2°.”

For a humanoid robot: the action could be “bend the left knee.”

4.1 Action is how the agent changes the world
Every action has consequences. There is no such thing as an “effectless” action.

Without actions, the agent does not influence the environment, so it cannot learn anything.

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5. Reward

At a fundamental level, reward is the only way the agent understands whether it has done something good or bad.

An agent has no “morality” or “natural goals”. Reward is the only channel through which we tell it what is valuable and what is not. Without reward, the agent cannot learn anything.

5.1 The reward does not say “what to do”, but only “how it did“
Reward is NOT an instruction. It is feedback about consequences, not actions. The agent must deduce for itself “what action led to this result.”

5.2 A correct reward completely controls the personality and behavior of the agent
Under the same conditions, a different reward makes the agent have a completely different behavior. Reward is the DNA of the RL agent. If you want different behavior, you don’t change the algorithm, you change the reward.

5.3 Reward is local in time, but learning is global in time
The agent receives a reward at a specific moment. But try to maximize the long-term consequences. Rewards don’t make sense without temporal context.

5.4 Rewards can be misleading or ambiguous
Rewards don’t guarantee that the agent understands your real intention. The agent optimizes “blindly” what you give it. This is where problems like “reward hacking” come in.

5.5 A major factor in the failure of an RL project is using the wrong reward
The reward reflects how you define the problem. If the problem is poorly defined, the agent will create the wrong behavior.

5.6 The reward must be tied to the real goal, not to what seems easy to measure
If you reward what you can measure and not what matters, the agent learns exactly what you tell it, not what you would have wanted it to say.

5.7 Rewards are a numerical signal, not a rule
The agent doesn’t get “if you see a tree, avoid it.” It gets “-1 when you hit a tree.” This is also a fundamental difference in ML. RL learns behavior, not rules.

5.8 The reward doesn’t have to be perfect for RL to learn
The reward can be imperfect, partial, noisy. RL can learn even with incomplete feedback. The important thing is that the direction is correct.

5.9 A good reward is always tied to the final goal
RL maximizes “what you want,” not “what you can mathematically prove.”

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6. Policy

Policy is the agent’s decision-making strategy. Policy is not an algorithm. It is not a list of rules. It is not a formula. It is how the agent decides what action to take in each situation. It is the agent’s behavioral “personality.”

6.1 Policy defines the agent’s behavior completely
If you want to describe an RL agent in two words, explain its policy.

Everything the agent does – the way it moves, avoids obstacles, accelerates, explores – comes from the policy.

Policy maps perception (state) into action (action). Policy answers just one question: “Given the current situation, what should I do?”

6.2 Policy is not programmed by hand, it is learned
This is a critical difference from classical programming:

• in a traditional robot, the programmer writes the rules,
• in RL, the agent learns the rules by experience.

6.3 Policy is the result of learning
A good policy is consistent. In similar situations, the agent:

• makes consistent decisions,
• reproduces efficient behaviors,
• behaves predictably and intelligently.

6.4 Consistency is the indicator of a healthy policy
Policy can be flexible and adaptive. A good policy is not rigid.

It learns to adapt to:

• sensor noise,
• environmental uncertainty,
• dynamic changes,
• completely new situations.

This makes RL something superior to scripting.

6.5 Policy can include elements of exploration
Sometimes the policy does not choose the “best known” action, but new actions in order to learn.

This flexibility is part of the RL process.

6.6 The whole purpose of RL is to improve the policy
All updates, all experiences, all episodes…all have one final goal: a better policy.

RL is the process of transforming experiences into strategies.

6.7 Policy does not memorize actions, it generalizes behavior
The agent does not remember every situation. Instead:

• learns a pattern of behavior,
• works well in new situations,
• generalizes, does not memorize.

This makes it useful in the real world.

6.8 Policy is the bridge between learning and action
An agent can have:

• reward,
• value,
• return,
• exploration,
• but without policy, it cannot act.

Policy is where learning becomes behavior.

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7. Value

Value tells us how much a situation (or action) is worth in the long run.

Value does not describe what is happening now, but “what could happen,” on average, if the agent continues to act intelligently. It is about future potential, not the present moment.

7.1 Value is how the agent anticipates the future
An RL agent does not live only in the moment. Value allows it to imagine the future consequences of a present action. Without value, the agent would only make impulsive decisions.

7.2 Value connects immediate rewards with future rewards
Reward informs you right now. Value informs you long term. RL does not maximize the reward in the present, but the total potential.

7.3 Value is an internal guide to decision-making
Policy decides the action, but value tells whether that action is worth it. Value is the agent’s internal compass.

7.4 Value is an estimate, not an absolute truth
The agent learns to approximate value on its own:

• does not know the exact certainty of the outcomes,
• does not know all the consequences,
• learns from experience.

7.5 The agent evolves with its estimates
Value allows the agent to resolve the trade-off between “now” and “later.”

Sometimes:

• a small reward now –> a large reward later,
• a large reward now –> a disastrous ending.

Value allows the agent to choose intelligently between these options.

7.6 Value is what defines intelligent behaviors
A robot that:

• avoids risks,
• plans ahead,
• navigates efficiently,
• makes “good” decisions,

…does all of this because value tells it “what it’s worth.”

7.7 Value is the foundation of RL algorithms (directly or indirectly)
Even when you don’t see it explicitly (e.g. in the policy gradient), value is present as a principle:

• if you don’t estimate the future, you can’t learn optimal behavior,
• if you don’t estimate the consequences, you can’t plan.

7.8 Value is the basis of any sequential intelligence
Value allows the agent to generalize from past experiences. The agent learns the value of “types of situations,” not each situation separately. This way, it can react well in new scenarios.

7.9 Value transforms RL from reaction to anticipation
Without value, the agent only reacts to the reward. With value, the agent becomes able to:

• anticipate,
• plan,
• optimize,
• decide strategically.

This makes RL different from any other type of AI.

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8. Return

Return represents the full consequences of an episode, not just a moment.

8.1 Reward is what is happening now
Return is the sum of all the consequences from the beginning to the end of the episode. It is like a big picture.

8.2 Return defines whether a behavior was good or bad in the long run
An episode is “good” if its total rewards are large. The agent learns from this which strategies produce solid results, not just momentary successes.

8.3 Return is the ultimate goal in RL
All RL algorithms, no matter how complex, pursue the same thing: maximizing the total return.

This is the fundamental mission of any RL agent.

8.4 Return connects current actions with the future
A decision made now can:

• produce immediate reward,
• produce reward in the future,
• or may have negative consequences in a few moments.

8.5 Return takes all of these consequences into account
Without return, the agent would only live in the moment. It would always choose actions that give instant reward, even if:

• they are risky,
• they are bad in the long run,
• they lead to failure.

Return tells the agent: “It’s not just now that matters. Everything that happens after this decision matters.”

8.6 Return is the only real measure of the success of a policy
It doesn’t matter how smart an agent seems. It doesn’t matter how well he moves. It doesn’t matter how spectacular his actions are.

The only objective measure is: how much return his policy produces, on average.

8.7 Return explains why RL is about chains of events, not isolated actions
In RL, actions are not independent. Each action changes the future state, which changes the future reward, which changes the total return.

8.8 Return distinguishes between safe behaviors and risky behaviors
An agent that maximizes only immediate reward:

• accelerates too fast,
• cuts corners,
• avoids exploration,
• ignores dangers.

An agent that maximizes return:

• plans,
• avoids unnecessary risks,
• seeks stability,
• learns robust behaviors.

Return is not determined by the agent, but by the problem.

The programmer decides:

•  the beginning of the episode,
• the end,
• what success means,
• what failure means,
• what rewards are given.

The return reflects these choices.

8.9 Return is the final truth that the agent follows

The agent can be “fooled” with ill-defined rewards, but it cannot be “fooled” in what it pursues: return is the ultimate goal of RL.

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9. Exploration

Exploration is how the agent discovers new things. An agent cannot know from the start what is good or bad. Exploration is the process by which it tries new actions to see what consequences they produce.

9.1 Exploration is necessary to find better behaviors than those already known
An agent that only repeats what it already knows remains stuck in a mediocre strategy.

Exploration allows it to:

• discover better paths,
• learn new situations,
• overcome the limitations of a weak policy.

9.2 Exploration involves risk, but it is a controlled risk
When the agent tries new things:

• sometimes finds good solutions,
• sometimes it makes mistakes.

But these mistakes are necessary for progress.

9.3 Exploration is the RL response to uncertainty
 In the real world, the environment:

• is unpredictable,
• does not follow fixed rules,
• has noise and variations.

Exploration is the agent’s way of adapting to the unknown.

9.4 Exploration and exploitation are a balance, not extremes
The agent must choose between:

• Exploration – I try new things,
• Exploitation – I apply what I already know works.

Success in RL comes from the balance between the two, not from choosing just one.

Exploration means the agent’s courage not to be satisfied with the first solution found. An intelligent agent:

• does not attach itself to a weak strategy,
• continues to experiment occasionally,
• constantly seeks improvements.

Exploration is how the agent builds its map of the environment. The agent is not given a map of the world. It discovers it progressively, through experience.

Each exploratory action = a new point on the map.

9.5 Without exploration, RL becomes the equivalent of a fixed function
An agent that does not explore:

• always repeats the same behavior,
• does not find new solutions,
• cannot generalize,
• does not progress.

9.6 Exploration is the engine of change
Exploration is vital in a real environment, not just in simulations. In simulations, you can control everything.

In reality:

• unexpected obstacles appear,
• sensors make noise,
• friction changes,
• conditions change.

9.7 Exploration allows the agent to learn robust behaviors
Exploration defines the maturity of a policy. At first, the agent explores a lot. As it learns, it explores less. This is the signature of an agent that is behaviorally maturing.

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A Robot Example: From Raw Sensors to Smart Decisions

Imagine a 2WD robot in an orchard. All around it are trees, grass, bumps, areas of shade, leaves moved by the wind.

The robot, or rather the agent that controls the robot, does not see the world like a human. It has no visual concepts, it does not know what a tree, a road or an obstacle is. All that reaches it are numbers.

And yet, over time, the robot learns to accelerate, slow down, avoid, turn, advance towards the goal.

How is this possible?

Through Reinforcement Learning, raw signals are transformed into intelligent behavior.

Here is how this process happens, seen at the most fundamental level.

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What goes into the robot (raw signals)

The robot receives at any given moment only:

• approximate distances from LiDAR –> sometimes accurate, sometimes noisy,
• accelerations and rotations from IMU –> affected by drift and vibrations,
• wheel speeds from encoders –> can slip on grass or mud,
• different light intensities from the front camera,
• imprecise proximity signals.

These values ​​are incomplete, imperfect, fluctuating. And yet, they are enough. They don’t have to be perfect, they just have to indicate the direction in which reality is moving.

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How the robot transforms raw values ​​into “what it thinks about the world”

The robot cannot make decisions directly from signals. It needs to form, very simply, an idea about:

• how close an obstacle is,
• how oriented it is towards the goal,
• how stable it is moving,
• if it is slipping,
• if it is accelerating too quickly,
• if it is turning too sharply.

This is not the complete reality. It is the filtered reality, enough to make a useful decision.

The robot, from three or four raw signals (e.g. distance + rotation + speed + direction), builds its own “simplification” of the world.

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How does the robot choose a concrete action (left, right, forward, stop)

The robot does not know from the beginning which action is correct.

At each moment it decides only according to the perceived situation:

• if the distance decreases quickly –> it turns,
• if the wheel slips –> it reduces speed,
• if the road is clear –> it accelerates,
• if the sensors become unstable –> it slows down and seeks stability.

It is not a manually programmed rule. It is an evolving strategy.

At first, the robot does strange things:

• it turns too late,
• it accelerates too much,
• it gets stuck between obstacles,
• it approaches the goal chaotically.

But each such action has consequences.

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How the robot feels the consequences (reward –> change of strategy)

After each action, the robot learns whether the consequence was good or bad – but not in words, in a number.

• avoided the obstacle –> small positive reward,
• got closer to the goal –> bigger reward,
• ran into the tree –> penalty,
• got stuck –> penalty,
• advanced efficiently –> bonus.

You don’t tell it what to do. You just tell it how it did.

Gradually, the robot begins to notice:

• “when you turn early, you are safer,”
• “when you accelerate in the mud, you slip,”
• “when you stay too close to obstacles, you get penalties,”
• “when you keep the right direction, you win in the long run.”

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How behavior changes over time (from chaotic to intelligent)

As the robot gains experience:

• collisions become rare,
• steering becomes smoother,
• adjustments are finer,
• braking is anticipated,
• turns are smoother,
• decisions are faster.

Not because it uses more sensors. Not because it has been given a new algorithm. But because it has accumulated chains of experiences that have told it “what it deserves.”

The robot learns, over time:

• to maintain a safe space,
• to avoid unstable areas,
• to seek efficient paths,
• to adjust acceleration depending on the terrain,
• to ignore moments when a sensor “lies”,
• to behave robustly, not fragilely.

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Why this flow works because of RL (and not because of programming)

If you tried to program it manually:

• if it’s muddy → reduce speed,
• if you see a tree → turn,
• if the sensor is noisy → slow down,
• …

you would have to write thousands of rules. And it still wouldn’t work in reality.

RL has no such limitations because:

• learns adaptively (when the terrain changes, the robot adjusts its policy),
• learns from consequences (it doesn’t need to know the model of the environment),
• learns robustness (sensor noise doesn’t destroy the behavior, it refines it),
• learns generalization (it works in situations it hasn’t seen before).

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The Hidden Mistakes That Break RL Projects

1. Incorrect problem definition (the biggest reason for failure)

Most RL projects fail before they even start, because:

• the problem is not formulated as a decision process over time,
• the episode does not have a clear beginning and end,
• there is no understanding of what success means.

RL cannot solve a vague problem. If the problem is not formulated correctly, no algorithm will save you.

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2. A poorly defined reward destroys behavior

In RL, the reward is the real goal of the project. If the reward does not reflect the goal:

• the agent optimizes something else,
• absurd behaviors appear,
• reward hacking occurs,
• the agent becomes unstable or exploits bugs,
• learning completely crashes.

RL learns EXACTLY what you tell it – not what you wanted it to say.

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3. Useless or incomplete observations (garbage in –> garbage policy)

A massive mistake is that:

• the agent has too many observations as inputs (redundancy, noise, confusion)
  or
• or has too few (the agent is “blind“).

RL does not have a magical memory. RL can only decide from the information it has at that moment.

If the observations are not relevant to the decision, RL cannot invent their absence.

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4. Incorrect definition of actions

Wrong actions result in:

• too many actions –> the agent cannot explore efficiently,
• too few actions –> the agent cannot express useful strategies,
• physically impossible actions –> the simulator explodes,
• contradictory actions (e.g. “accelerate + brake simultaneously“).

Actions are the language through which the agent communicates with the world. If the language is poorly chosen, communication is impossible.

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5. The environment is unstable or uncontrolled

A simulation that is not realistic leads to:

• unstable physical collisions,
• poorly configured rigid-body models,
• exploding forces,
• unstable joints,
• objects that bounce unnecessarily,
• simulators with variable delta time.

If the environment is unstable, the agent does not learn, or the agent learns chaos. This is a systemic problem, not a bug in RL.

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6. Without proper resetting of the episode –> the agent learns aberrant behaviors

During training, the resetting must:

• put the robot in realistic situations,
• diversify the initial conditions,
• prevent the memorization of a single solution,
• avoid impossible positions,
• reset the velocities, not just the positions.

If the resetting is wrong, the agent learns “only part of the game”, not the whole game.

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7. Lack of diversity → agent becomes “learned by heart”

RL does not generalize automatically. RL ONLY generalizes to the variations it has seen.

If training is identical for each episode:

• agent learns the route, not the navigation,
• agent learns a sequence, not a behavior,
• agent becomes brittle in real conditions.

This is why domain randomization is essential.

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8. Insufficient exploration –> agent does not discover better solutions

If the exploration/exploitation parameter is chosen wrongly:

• exploration too small –> stuck behavior,
• exploration too large –> agent does not converge.

Without controlled exploration, RL is just a reaction function, not a learning process.

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9. Algorithm choice is overrated (and almost irrelevant at the beginning)

RL is not just about algorithm choice. On the contrary, RL is about:

• a good algorithm does not fix a bad reward,
• a good algorithm does not fix an incorrect environment,
• a good algorithm does not fix wrong observations,
• a good algorithm does not fix impossible actions.

The algorithm only matters after everything else is correct.

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10. Lack of a clear “success/failure” criterion

RL needs:

• a moment when the episode ends,
• a clear failure condition,
• a clear success condition.

Without them, the agent wanders aimlessly. Maximize local rewards, not returns.

RL cannot learn a game if you do not define its ending.

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Reinforcement Learning Is Not an Algorithm, It’s a Relationship

Most documentation presents RL as:

• a formula,
• an algorithm (PPO, SAC, Q-Learning),
• a code,
• a mechanical process.

This means that someone who wants to apply RL needs to choose the right algorithm.

In reality, RL is a type of relationship between two elements:

• what the agent does,
• what happens after that.

Everything that happens in RL can be understood through this fundamental connection:

action –> consequence –> adjustment

This is the relationship. The algorithm is just how this relationship is optimized.

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 1. RL is not a formula, but an evolving behavior

Formulas (Bellman, Q-values, advantage) are not RL. They are just tools to model the relationship.

RL is the change of behavior over time. When the agent hits a corner, it avoids the corner next time – that’s RL.

When the agent sees a shortcut and prefers it – that’s RL.

When the robot learns to slip less in the mud – that’s RL.

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 2. RL is a relationship between “what I did” and “what I got”

RL is the only branch of ML where:

• there is no teacher,
• there are no labels,
• there is no right answer,
• there is no “ground truth”.

The only thing that connects the past to the future is the answer to the question: how did what you just did affect you?

No other branch of ML has this kind of direct causality.

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 3. RL is about understanding long-term consequences

Most good things in life happen:

• not immediately,
• but after a series of good choices.

RL reflects this reality exactly:

• sometimes you get a penalty today for a big reward tomorrow;
• sometimes a small reward today is a trap in the long run;
• RL teaches the balance between the present and the future.

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 4. RL is a relationship between agent and environment, not a list of rules

RL does not teach “if you see X –> do Y.” RL learns general patterns of the relationship:

• “when obstacles approach, slow down a little,”
• “when you slip, adjust your direction,”
• “when the ground is smooth, speed up.”

These are not programmed rules. They are emergent behaviors.

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 5. Each experience modifies the relationship a little

RL does not learn all at once. The relationship between action and outcome gets stronger little by little.

At first, the robot is chaotic.

After a few hundred episodes, the robot becomes predictable.

After a few thousand, the robot becomes stable and efficient.

This gradual process is exactly the relationship in action.

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 6. The algorithm is just the tool, not the essence of RL

Those who want to develop applications with RL, often try to find the answer to:

• “PPO or SAC?”
• “which hyperparameters?”
• “which network?”

But this is a complete reversal of priorities.

RL is about actions, consequences and adjustments. The algorithm just decides how the adjustments are made.

• PPO adjusts more smoothly.
• DQN adjusts values.
• SAC adjusts stochastic policies.

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7. RL is about emergent behavior

You don’t program:

• how to turn,
• how to slow down,
• how to manage risk.

You don’t tell it “what to do.” You just tell it “how you did.”

All intelligent behavior emerges because the relationship between action and consequence improves over time.

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8. RL works because the world has feedback

If actions didn’t have consequences, RL wouldn’t exist.

RL is based on:

• the laws of physics (path, forces, friction),
• robot dynamics,
• mechanical limitations,
• environmental reactions.

That is, RL draws its learning directly from reality, not from theoretical models.