{"id":1091922,"date":"2024-10-09T12:32:14","date_gmt":"2024-10-09T19:32:14","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1091922"},"modified":"2024-10-09T12:32:14","modified_gmt":"2024-10-09T19:32:14","slug":"implicit-curriculum-in-procgen-made-explicit","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/implicit-curriculum-in-procgen-made-explicit\/","title":{"rendered":"Implicit Curriculum in Procgen Made Explicit"},"content":{"rendered":"
Procedurally generated environments such as Procgen Benchmark provides a testbed for evaluating the agent’s ability to robustly learn a relevant skill, by situating the agent in ever-changing levels. The diverse levels associated with varying contexts are naturally connected to curriculum learning. Existing works mainly focus on arranging the levels to explicitly form a curriculum. In this work, we take a close look at the learning process itself under the multi-level training in Procgen. Interestingly, the learning process exhibits a gradual shift from easy contexts to hard contexts, suggesting an implicit curriculum in multi-level training. Our analysis is made possible through C-Procgen, a benchmark we build upon Procgen that enables explicit control of the contexts. We believe our findings will foster a deeper understanding of learning in diverse contexts, and our benchmark will benefit future research in curriculum reinforcement learning.<\/p>\n","protected":false},"excerpt":{"rendered":"
Procedurally generated environments such as Procgen Benchmark provides a testbed for evaluating the agent’s ability to robustly learn a relevant skill, by situating the agent in ever-changing levels. The diverse levels associated with varying contexts are naturally connected to curriculum learning. Existing works mainly focus on arranging the levels to explicitly form a curriculum. In 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