(Reinforcement Learning): In reinforcement learning, the reward is the feedback provided to an agent when a specific action is taken at a particular state in the environment. Using the concept of “trial and error” for reinforcement learning, the rewards are the “errors”. For example, if an agent is trying to find a solution to a maze, the reward would be a positive point if it makes progress towards the solution in a maze, and a negative point if it encounters an obstacle or backtracks on an existing path.
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