Coordinating optimization stability, model-based representation, and scheduled prioritized replay yields larger sample-efficiency gains than naively stacking the same components in continuous-control RL.
Beyond the rainbow: High perfor- mance deep reinforcement learning on a desktop PC
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Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control
Coordinating optimization stability, model-based representation, and scheduled prioritized replay yields larger sample-efficiency gains than naively stacking the same components in continuous-control RL.