Model-Based Reinforcement Learning

Model-based RL learns a model of the environment's dynamics and uses it to plan or generate simulated experience.

Learning the environment

Model-based reinforcement learning learns an approximate model of the environment — how states transition and what rewards follow — and uses that model to plan or to generate imagined experience. This contrasts with model-free methods, which learn a policy or value function directly from real interaction without ever modeling the dynamics.

What the model provides

Kronos motion — pid vs model

A transition model predicting the next state from state and action.
A reward model predicting reward for a transition.
Together they let the agent simulate outcomes without touching the real environment.

Using the model

Once learned, a model can be used two ways. In planning, the agent searches over imagined action sequences to choose the best, as in model-predictive control or Monte Carlo tree search. In Dyna-style learning, the model generates synthetic transitions that are mixed with real ones to train a value function or policy, multiplying the value of each real interaction.

The sample-efficiency payoff

Model-based methods are often far more sample efficient than model-free ones because each real interaction improves the model, which can then be queried many times for free. This matters when real interaction is slow, expensive, or risky — precisely the regime of physical-system control, where a validated simulator serves as the model and lets controllers be developed without touching hardware that may not yet exist.

The catch

The central risk is model bias: planning against an inaccurate model can produce policies that exploit its errors and fail in reality. Modern methods manage this by quantifying model uncertainty, planning only over short horizons where the model is trusted, or using ensembles of models to avoid overconfident predictions.