Questions tagged [model-based-methods]

For questions about model-based reinforcement learning methods (or algorithms). An example of a model-based algorithm is Dyna-Q, which estimates a model of the environment (i.e. the transition function of the associated Markov decision process).

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What's the difference between model-free and model-based reinforcement learning?

What's the difference between model-free and model-based reinforcement learning? It seems to me that any model-free learner, learning through trial and error, could be reframed as model-based. In ...
1
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2answers
434 views

How can we estimate the transition model and reward function?

In reinforcement learning (RL), there are model-based and model-free algorithms. In short, model-based algorithms use a transition model (e.g. a probability distribution) and the reward function, even ...
5
votes
3answers
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Isn't a simulation a great model for model-based reinforcement learning?

Most reinforcement learning agents are trained in simulated environments. The goal is to maximize performance in (often) the same environment, preferably with a minimum amount of interactions. Having ...