Regret
Concept
In reinforcement learning, regret is the difference between the performance of an optimal policy and the performance of the agent's current policy on a given environment. It indicates the potential for learning.
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In reinforcement learning, regret is the difference between the performance of an optimal policy and the performance of the agent's current policy on a given environment. It indicates the potential for learning.
