TEMPORAL DIFFERENCE LEARNING Meaning and
Definition
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Temporal difference learning is a method used in reinforcement learning, which involves training an agent to make decisions in an environment based on trial and error. In temporal difference learning, the knowledge or value function of the agent is updated by taking into account the difference between the expected and actual rewards received during each time step.
Specifically, temporal difference learning algorithms estimate the value of a state or action by making use of the difference between the value estimates of subsequent states. This difference is known as the temporal difference error. By iteratively updating the value estimates based on these errors, the agent gradually improves its knowledge and becomes more capable of making optimal decisions.
Temporal difference learning is often implemented using a technique called temporal difference control, where the agent learns online by updating its value function after each action or state transition. This allows the agent to learn from incomplete and delayed feedback, as it gradually refines its predictions by comparing them with actual outcomes.
The main advantage of temporal difference learning is its ability to handle complex and stochastic environments, where the rewards and outcomes may be uncertain. It also enables the agent to learn from experiences and adapt its behavior over time. As a result, temporal difference learning has been successfully applied in a variety of domains, including game playing, robotics, and financial modeling.
