Temporal difference learning combines ideas from Monte Carlo methods and dynamic programming for model-free reinforcement learning.
Temporal difference learning is used in reinforcement learning tasks where an agent learns to predict the value of a given state based on the rewards received over time. It works by updating the value of a state based on the difference between predicted rewards and actual rewards received, hence the term “temporal difference.”
For example, in a game scenario, an agent might predict the value of a state based on past experiences. As the game progresses, the agent updates its predictions based on the actual rewards received, refining its understanding of the value of each state.
The main advantage of temporal difference learning is that it allows for learning directly from raw experience without a model of the environment. It is particularly useful in situations where the environment is stochastic or partially observable.
Temporal difference learning is important because it provides a foundation for many reinforcement learning algorithms, including Q-learning and SARSA. Thus it is a key concept for understanding how agents can learn to make decisions over time based on experience.
- Alias
- TD-learning TD(0)
- Related terms
- Reinforcement Learning Markov Decision Process