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 ...
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vote
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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 ...
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votes
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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 ...