Questions tagged [model-free-methods]

For questions about model-free reinforcement learning methods (or algorithms). An example of a model-free algorithm is Q-learning, which does not use the transition function (i.e. the model) of the environment (or 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
vote
2answers
500 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 ...
2
votes
1answer
110 views

Why are state-values alone not sufficient in determining a policy (without a model)?

"If a model is not available, then it is particularly useful to estimate action values (the values of state-action pairs) rather than state values. With a model, state values alone are sufficient ...