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We continue our series on the MQL5 wizard, where lately we are alternating between simple patterns from common indicators and reinforcement learning algorithms. Having considered indicator patterns (Bill Williams’ Alligator) in the last article, we now return to reinforcement learning, where this time the algorithm we are looking at is Proximal Policy Optimization (PPO). It is reported that this algorithm, that was first published 7 years ago, is the reinforcement-learning algorithm of choice for ChatGPT. So, clearly there is some hype surrounding this approach to reinforcement learning. The PPO algorithm is intent on optimizing the policy (the function defining the actor’s actions) in a way that improves overall performance by preventing drastic changes that could make the learning process unstable.

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