Online Course Support | Practical Reinforcement Learning

What are the differences between model-free and model-based settings?

ByAdmin October 9, 2021October 9, 2021

In a model-based setting, we know nothing about environment dynamics. An agent is learning by optimizing some parametric model.

In a model-based setting, we can find out which (reward, next state) pairs are possible given current state and action.

In a model-free setting, we know nothing about environment dynamics. Optimization of agent decisions is based solely on sample-based experiences of the world.

Model-free learning requires probabilities of possible next states and rewards but does not require a sample model (aka simulator, aka generative model)

In a model-free setting, we know which (reward, next state) pairs are possible given current state and action.

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For long episodes and very short planning time RP should be as fast as possible, TP can be much slower.

RP and TP are conflicting policies — the one that performs the best becomes the final result of planning.

Random RP is best, since improving the quality of RP may reduce the quality of MCTS planning.

MCTS is relatively insensitive to the quality of RP. So we can use random RP, but it is always better to make RP as good as possible.

For long episodes and short planning time TP should be as fast as possible, RP can be much slower.

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