Temporal difference learning is a type of reinforcement learning where an agent learns to predict future rewards by comparing its current estimate of the value of a state with its subsequent experience. This method allows the agent to update its predictions based on the difference between expected and received rewards, helping to refine its decision-making process. It connects closely with reward-modulated plasticity, as the changes in synaptic strength are influenced by reward feedback, shaping the behavior and choices of the agent over time.
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