Real-World Applications of Q-Learning: Gentle Introduction


Reinforcement learning, as per our recent post, deals with a unique problem setup where an arbitrary agent is trying to learn the optimal way of interacting with an environment. In return for its actions, it receives delayed labels also known as rewards; the agent’s ultimate goal is to find an optimal policy that maximizes cumulative numerical return.

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