Explain the concept of temporal difference (TD) learning.
Temporal Difference (TD) learning is a fundamental concept in reinforcement learning (RL) that combines ideas from dynamic programming and Monte Carlo methods to learn value functions and optimal policies directly from experience without requiring a model of the environment's dynamics.
TD learning updates value estimates based on the observed transitions between states and the rewards received at each time step.
Here's an explanation of the concept of TD learning :
* Prediction of Value Functions
* Temporal Difference Error
* Value Function Update
* Advantages of TD Learning
* Applications
