Exploration vs. Exploitation
A core problem in reinforcement learning referring to the trade-off an agent faces between trying new actions to discover potentially better strategies (exploration) and applying already-known optimal strategies to obtain rewards (exploitation).
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Exploration vs. exploitation is the core challenge that distinguishes RL from supervised learning: the agent needs to trade off between trying new actions to gain information and exploiting the known best action to obtain rewards.
The exploration-exploitation trade-off directly determines whether an algorithm can converge to the global optimum, and it is also the main subject of study in the multi-armed bandit problem.
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Exploration vs. exploitation is the core challenge that distinguishes RL from supervised learning: the agent needs to trade off between trying new actions to gain information and exploiting the known best action to obtain rewards.
50%UnverifiedThe exploration-exploitation trade-off directly determines whether an algorithm can converge to the global optimum, and it is also the main subject of study in the multi-armed bandit problem.
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