ACED-DQN combines heterogeneous DQN variants with loss-based reliability weighting and experience assignment, but the paper's own ablation indicates that arbitration control is not the key factor behind the performance gain.
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An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
ACED-DQN combines heterogeneous DQN variants with loss-based reliability weighting and experience assignment, but the paper's own ablation indicates that arbitration control is not the key factor behind the performance gain.