Temporal Difference Learning
Temporal Difference (TD) Learning is a fundamental technique in the field of reinforcement learning (RL), which is a branch of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. TD learning is model-free, meaning it does not require a model of the environment (i.e., the transition probabilities and rewards for all state-action pairs).
Instead, it learns directly from experience, updating value function estimates based on the difference (or "temporal difference") between subsequent predictions. This approach combines ideas from Monte Carlo methods, which learn from complete episodes, and dynamic programming methods, which bootstrap by using current value estimates to update new value estimates. TD learning is particularly known for its efficiency and ability to learn online, updating estimates based on individual experiences without needing to wait for the outcome of an entire episode.
A classic example of TD learning is the TD(0) algorithm applied in the context of a simple game like tic-tac-toe or a more complex environment such as navigating a maze. In tic-tac-toe, for instance, the agent (player) learns the value of each board position based on the outcome of games.
After each move, the agent updates its value estimate for the previous state based on the current estimate and the reward received (which may be intermediate or final, such as winning, losing, or drawing the game).
This incremental updating allows the agent to gradually improve its policy, guiding it toward making moves that are more likely to lead to winning the game. In a maze, the agent would learn the value of each position (state) in terms of its potential to lead to the goal, updating its estimates as it navigates the maze and receives feedback based on its proximity to the goal, obstacles, or penalties.