Almost all tutorials, books, and courses tell RL learners:
Define a Markov Decision Process: states, actions, transitions, rewards. That’s where it starts. Done.
But the hidden story of creating a working Markov Decision Process (MDP) is that it’s not a simple checklist.
It’s that every element of the MDP is decisive in terms of whether your agent learns or fails. And no one tells you how fragile and subtle this step actually is.
1. The MDP is a story you tell the AI agent about reality, it’s not the reality
- Reality is continuous, noisy, high-dimensional, containing a lot of unknowns.
- An MDP is a simplified fiction where you choose what counts as a “state” and what counts as an “action.”
Your MDP is always a lossy restriction of reality. The secret is choosing which details to keep and which to ignore.
2. The state space decides if learning is possible or not
- We are entering an area of the punishment of dimensionality. In other words, the agent never generalizes.
- Too few states, and the agent is blind to the information it needs to act. The agent doesn’t learn.
State design is not about completeness, it’s about sufficiency. We need to give the agent just enough states to make good decisions, but not so much so that it drowns.
3. The Markov assumption almost never holds
- An MDP assumes the future depends only on the current state.
- In real tasks, partial observability is everywhere. Think of robotics: sensors are delayed, noisy, incomplete.
- You end up with a POMDP (partially observable MDP), but many people just pretend it’s an MDP and hope.
Most “MDPs” in practice are approximations that cheat on the Markov assumption. Success often depends on how well you fake it.
4. Actions are politics
- Define actions too narrowly and the agent can’t discover creative solutions.
- Define them too broadly and the search space explodes, training becomes impossible.
Action space design is a negotiation between what’s “natural” for the problem and what’s “feasible” for the algorithm.
5. Rewards distort the MDP itself
- Rewards aren’t just an external signal, they change the dynamics of what matters.
- An agent doesn’t learn “the world”, it learns the world through the lens of your reward.
Every reward turns your MDP into a biased mirror of reality, and your agent is living inside that mirror, not the world you thought you described.
6. Transitions are where the real mess hides
- In textbooks: “P(s’|s, a)” is clean.
- In reality: physics engines are buggy, sensors lag, actuators have delays.
- Your transition model is never perfect, it’s always a leaky abstraction.
Most RL work isn’t “learning in a clean MDP,” it’s patching around the cracks in the transition dynamics you can’t model correctly.
7. An MDP is a design choice, not a discovery
This is the big secret:
- There’s no single “true” MDP for a problem.
- You invent one, shaped by what you think matters.
- A different designer might invent a totally different MDP for the same task. Both of them could work, or fail. It depends on what the agent can handle.
An MDP is not reality’s framework, it’s a framework seen through the eyes of the designer. A bad MDP dooms the agent before training even starts.
Conclusion
Creating a good MDP is not about applying a formula. It’s about storytelling, engineering trade-offs, and selective blindness. It’s not about capturing “the truth of the world,” but about creating a useful “reality” that your agent can actually learn from.
Do you want to learn how to implement a Markov Decision Process (MDP) in a practical and efficient way? I’ve created a detailed guide that will help you navigate the complexity of this fundamental concept in Reinforcement Learning.
