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Explain the differences between model-based and model-free reinforcement learning.
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Answer Posted / Brijesh Singh
Model-Based Reinforcement Learning (MBRL) constructs a predictive model of the environment, using it to choose actions by simulating possible futures. This approach allows for planning and foresight, but can be computationally expensive due to the need to maintain and update the model. On the other hand, Model-Free Reinforcement Learning (MFRL) does not explicitly build an environment model, instead it learns the reward function directly from interactions with the environment through trial and error. MFRL is generally more scalable but lacks the ability to plan ahead.
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