mql5

We continue the theme of environmental exploration in reinforcement learning. In previous articles within this series, we have already looked at algorithms for exploring the environment through curiosity and disagreement in an ensemble of models. Both approaches exploited intrinsic rewards to motivate the agent to perform different actions in similar situations while exploring new areas. But the problem is that the intrinsic reward decreases as the environment gets better explored. In complex cases of rare rewards, or when the agent may receive penalties on the way to the reward, this approach may not be very effective. In this article, I propose to get acquainted with a slightly different approach to studying the environment – the Go-Explore algorithm.

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