Model-Based Reinforcement Learning
This approach to Reinforcement Learning (RL) involves the agent learning a model of the environment's dynamics, predicting how actions will affect future states and rewards. Instead of directly learning a policy by trial and error, the agent can use this learned model to plan future actions or even simulate scenarios, significantly improving sample efficiency. It contrasts with model-free methods that learn directly from interactions.
It's like a chess player who can mentally simulate several moves ahead, rather than just reacting to the opponent's last move.
Reduces the amount of real-world interaction needed for an RL agent to learn, making it practical for complex and dangerous environments.
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