Equation 7 · From Scripted Bots to Autonomous Agents: A History of AI Agent Architecture
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The next architectural break replaced the hand-written operator library with something the agent acquired through trial, error, and a numeric reward signal, rather than something a person specified in advance. Sutton and Barto’s textbook formalizes the setting that every reinforcement-learning agent since has used: an agent and an environment exchanging a state, an action, and a scalar reward at each discrete time step, with the agent’s goal defined as maximizing cumulative reward rather than satisfying a hand-specified goal predicate [ 3 ] . The canonical learning rule for estimating the value of taking action in state , temporal-difference Q-learning, updates an estimate toward a…
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The next architectural break replaced the hand-written operator library with something the agent acquired through trial, error, and a numeric reward signal, rather than something a person specified in advance. Sutton and Barto’s textbook formalizes the setting that every reinforcement-learning agent since has used: an agent and an environment exchanging a state, an action, and a scalar reward at each discrete time step, with the agent’s goal defined as maximizing cumulative reward rather than satisfying a hand-specified goal predicate [ 3 ] . The canonical learning rule for estimating the value of taking action in state , temporal-difference Q-learning, updates an estimate toward a bootstrapped target rather than waiting for a final outcome:
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