R
Regret
ConceptMentioned in 1 video
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.
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.
