Hyperparameter Optimization Types
%2C%20where%20agents%20continuously%20interact%20with%20and%20adapt%20to%20their%20environments%2C%20requiring%20dynamic%20adjustments%20in%20their%20learning%20trajectories.%20To%20cater%20to%20this%20dynamicity%2C%20the%20Population-Based%20Training%20(PBT)%20was%20introduced%2C%20leveraging%20the%20collective%20intelligence%20of%20a%20population%20of%20agents%20learning%20simultaneously.%20However%2C%20PBT%20tends%20to%20favor%20high-performing%20agents%2C%20potentially%20neglecting%20the%20explorative%20potential%20of%20agents%20on%20the%20brink%20of%20significant%20advancements.%20To%20mitigate%20the%20limitations%20of%20PBT%2C%20we%20present%20the%20Generalized%20Population-Based%20Training%20(GPBT)%2C%20a%20refined%20framework%20designed%20for%20enhanced%20granularity%20and%20flexibility%20in%20hyperparameter%20adaptation.%20Complementing%20GPBT%2C%20we%20further%20introduce%20Pairwise%20Learning%20(PL).%20Instead%20of%20merely%20focusing%20on%20elite%20agents%2C%20PL%20employs%20a%20comprehensive%20pairwise%20strategy%20to%20identify%20performance%20differentials%20and%20provide%20holistic%20guidance%20to%20underperforming%20agents.%20By%20integrating%20the%20capabilities%20of%20GPBT%20and%20PL%2C%20our%20approach%20significantly%20improves%20upon%20traditional%20PBT%20in%20terms%20of%20adaptability%20and%20computational%20efficiency.%20Rigorous%20empirical%20evaluations%20across%20a%20range%20of%20RL%20benchmarks%20confirm%20that%20our%20approach%20consistently%20outperforms%20not%20only%20the%20conventional%20PBT%20but%20also%20its%20Bayesian-optimized%20variant.)
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