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 kind of reinforcement learning method does AlphaGo Deepmind use to beat the best human Go player?

In reinforcement learning, there are model-based versus model-free methods. Within model-based ones, there are policy-based and value-based methods. AlphaGo Deepmind RL model has beaten the best Go ...
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118 views

Why does Monte Carlo policy evaluation relies on action-value function rather than state-value function?

Here is David Silver's lecture on that. Look at 9:30 to 10:30. He says that, since it is model-free learning, the environment's dynamics are unknown, so the action-value function $Q$ is used. But ...
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93 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 ...
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1answer
90 views

Into which subcategories can reinforcement learning be divided?

In the course of a scientific work, I will discuss the different types of reinforcement learning. However, I have difficulties to find these different types. So, into which subcategories can ...
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1answer
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How can the policy iteration algorithm be model-free if it uses the transition probabilities?

I'm actually trying to understand the policy iteration in the context of RL. I read an article presenting it and, at some point, a pseudo-code of the algorithm is given : What I can't understand is ...
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How does policy evaluation work for continuous state space model-free approaches?

How does policy evaluation work for continuous state space model-free approaches? Theoretically, a model-based approach for the discrete state and action space can be computed via dynamic programming ...
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1answer
174 views

Is the minimax algorithm model-based?

Trying to get my head around model-free and model-based algorithms in RL. In my research, I've seen the search trees created via the minimax algorithm. I presume these trees can only be created with a ...
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1answer
709 views

Are model-free and off-policy algorithms the same?

In respect of RL, is model-free and off-policy the same thing, just different terminology? If not, what are the differences? I've read that the policy can be thought of as 'the brain', or decision ...
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1answer
625 views

Why are model-based methods more sample efficient than model-free methods?

Why do model-based methods use fewer samples than model-free methods? Here, I'm specifically referring to model-based methods in which we have to learn a policy and model. I can only think of two ...
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1answer
307 views

What is the relation between Monte Carlo and model-free algorithms?

Monte Carlo (MC) methods are methods that use some form of randomness or sampling. For example, we can use an MC method to approximate the area of a circle inside a square: we generate random 2D ...
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2answers
375 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 ...
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1answer
130 views

How do temporal-difference and Monte Carlo methods work, if they do not have access to model?

In value iteration, we have a model of the environment's dynamics, i.e $p(s', r \mid s, a)$, which we use to update an estimate of the value function. In the case of temporal-difference and Monte ...
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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 ...