Reinforcement Learning Algorithms

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What are the two types of RL algorithms?

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Model-based and model-free.

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What does a model-based algorithm do?

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Estimates the optimal policy using the transition and reward functions, learning a model of the environment’s dynamics.

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How does a model-free algorithm operate?

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It directly estimates the optimal policy through trial and error, using rewards received from the environment.

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When are model-free algorithms more suitable?

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In environments with complex dynamics where it is difficult to model the environment accurately.

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What is on-policy learning?

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A learning approach that requires the data used to train the policy to be generated by the same policy currently being improved.

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What is off-policy learning?

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A learning approach that allows the agent to learn from data generated by a different policy.

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What is the exploration-exploitation trade-off?

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The concept of exploiting what the agent already knows versus exploring a random action.