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(Reinforcement Learning): In reinforcement learning, the policy is the strategy that the agent will take to determine its next action based on the current state in the environment.  Using the concept of “trial and error” for reinforcement learning, the policy would be to know what trial to do next after a given error. For example, if an agent is trying to find a solution to a maze, the policy for the agent might be to move in a direction that it hasn’t already moved in as long as there is no obstacle preventing movement in that direction.