Foundations
Model-Based Reinforcement Learning
Model-based RL learns an explicit dynamics model of the environment and uses it for planning or synthetic data generation, trading model bias for dramatic gains in sample efficiency.
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AlphaGo needed around 5 million self-play games to surpass human Go players. A well-tuned model-based agent tackling the same problem can learn a comparable policy with orders of magnitude fewer environment interactions, because it spends most of its compute on a learned simulator rather than the real world. That asymmetry is the central argument for model-based reinforcement learning (MBRL).
The fundamental split: model-free vs. model-based
In model-free RL, the agent learns a value function or policy entirely from samples of real experience. Each gradient step consumes data collected by actually executing actions. The environment is a black box: you query it, observe (s, a, r, s'), and move on. No structure is assumed.
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