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This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. Q-learning is a reinforcement learning technique used in machine learning. The goal of Q-Learning is to learn a policy, which tells an agent what action to take under what circumstances. Q-learning finds a policy that is optimal in the sense that it maximizes the expected value of the total reward over all successive steps, starting from the current state. It does this by adding the maximum reward attainable from future states to the reward for achieving its current state, effectively influencing the current action by the potential future reward. This potential reward is a weighted sum of the expected values of the rewards of all future steps starting from the current state.
One strategy is to enter the train door as soon as they open, minimizing the initial wait time for yourself. This initially results in a longer wait time. However, entry slows, as time fighting other passengers to board is not rewarded. Q-Learning table of states by actions that is initialized to zero, then each cell is updated through training. However, Q-learning can also learn in non-episodic tasks.