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[D] should i over-sample rare episodes with successful exploration ?

I am using DRL (mostly policy gradients) in a simulated discrete sokoban-style environment.

Alex-the-agent is rewarded for the shortest possible solution, as well as training on progressively harder/intricate maps. After a while, exploration is very difficult, and it takes millions of attempts to complete an episode with a slightly-better score. To be clear, this is not a plateauing of performance, it just takes excessively longer exploration.

Should i be “over-sampling” these increasing-rare successful score improvements ?

I use PG since it works, but I am open to trying value techniques.

submitted by /u/so_tiredso_tired
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.