Model-free (reinforcement learning)

In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward function are often collectively called the "model" of the environment (or MDP), hence the name "model-free".

Source: Wikipedia — Model-free (reinforcement learning) (CC BY-SA 4.0)

Model-free (reinforcement learning)

In reinforcement learning (RL), a model-free algorithm is an algorithm which does not estimate the transition probability distribution (and the reward function) associated with the Markov decision process (MDP), which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward function are often collectively called the "model" of the environment (or MDP), hence the name "model-free".

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Source: Wikipedia "Model-free (reinforcement learning)" · CC BY-SA 4.0

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