Temporal Difference Learning: The Most Powerful RL Solution

Author(s): Rem E

Originally published on Towards AI.

Mastering the third and most widely used method in reinforcement learning

If you’ve been following along, you’re now ready to dive into the third, and most popular, solution method for RL problems: Temporal Difference (TD) Learning!
If not, check out the previous article first: Monte Carlo Off-Policy Explained.

The article explains Temporal Difference (TD) Learning, the powerful reinforcement learning (RL) method that combines the principles of Dynamic Programming and Monte Carlo to update value estimates at every time step instead of waiting until the episode ends. TD Learning addresses the exploration-exploitation trade-off in RL and is exemplified by the on-policy method SARSA. The article highlights the advantages of TD Learning, such as its efficiency and ability to learn in real-time, demonstrating its practicality and effectiveness in solving RL problems.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI