Discussing the article: "MQL5 Wizard Techniques you should know (Part 47): Reinforcement Learning with Temporal Difference"
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Check out the new article: MQL5 Wizard Techniques you should know (Part 47): Reinforcement Learning with Temporal Difference.
Temporal Difference is another algorithm in reinforcement learning that updates Q-Values basing on the difference between predicted and actual rewards during agent training. It specifically dwells on updating Q-Values without minding their state-action pairing. We therefore look to see how to apply this, as we have with previous articles, in a wizard assembled Expert Advisor.
The introduction to temporal difference (TD) learning in reinforcement learning serves as a gateway to understand how TD distinguishes itself from other algorithms, such as Monte Carlo, Q-Learning, and SARSA. This article aims to unravel the complexities surrounding TD learning by highlighting its unique ability to update value estimates incrementally based on partial information from episodes, rather than waiting for episodes to complete as seen in Monte Carlo methods. This distinction makes TD learning a powerful tool, especially where environments are dynamic and require prompt updates to the learning policy.
In the last reinforcement-learning article, we looked at the Monte Carlo algorithm that gathered reward information over multiple cycles before performing a single update for each episode. Temporal difference (TD) though, is all about learning from partial and incomplete episodes much like the algorithms of Q-Learning and SARSA that we tackled earlier on here and here.
Author: Stephen Njuki