Temporal-Difference (TD) learning is a popular approach for reinforcement learning. It involves updating an estimate of the value function, which represents the expected long-term reward of a particular state or action, based on observed rewards and the estimated value of subsequent states. TD learning uses the difference between the predicted and the actual reward, known as the TD error, to update the value function in real-time as the agent interacts with the environment.
The basic idea behind TD learning is that the value function should converge to the actual expected reward as the agent gathers more information about the environment. This process is repeated iteratively, with the agent adjusting its estimates of the value function based on the observed rewards and transitions from one state to another.
TD learning is particularly useful in situations where the reward is not immediately known, but rather must be inferred from subsequent states. This is because TD learning allows the agent to update its value function based on the estimated rewards of future states, rather than waiting for the actual reward to be received.
TD learning algorithms can be divided into two main categories: online TD methods and offline TD methods. Online TD methods update the value function incrementally as the agent interacts with the environment, while offline TD methods update the value function after each episode or after the agent has collected a sufficient amount of data.
Overall, TD learning is a powerful and flexible approach for reinforcement learning, and it has been applied to a wide range of real-world problems, including game playing, robot control, and recommendation systems.
Where to Learn More#
I’ve covered Temporal-Difference Learning for Reinforcement Learning in-depth in the following course: