temporaldifferensinlärning

Temporal difference learning is a core concept in reinforcement learning. It refers to a class of algorithms that learn by estimating the difference between successive temporal predictions. Instead of waiting for a final outcome, temporal difference methods update their value estimates as soon as a new piece of information becomes available. This makes them "model-free," meaning they do not need to know the underlying rules of the environment to learn.

The fundamental idea is to improve an estimate of a value function based on a sampled transition and the estimated value of the next state. For example, if an agent is trying to learn the value of being in a certain state, it can observe the immediate reward received and the value of the next state it transitions to. The difference between the immediate reward plus the estimated value of the next state and the current estimate of the state's value forms the "temporal difference error." This error is then used to update the current value estimate.

Common algorithms that utilize temporal difference learning include Q-learning and SARSA. These algorithms differ in how they use the temporal difference error to update the value function, specifically in how they estimate the value of the next state. Temporal difference learning is crucial for solving many complex reinforcement learning problems where the full reward signal may be delayed or sparse. Its ability to learn from experience as it unfolds makes it a powerful tool for sequential decision-making.