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A self-evolving default action can revolutionize how agents collaborate in continuous-action environments, ensuring unbiased learning and optimal convergence.
Forget hand-engineered reward shaping: ACDC learns robotic manipulation tasks faster and more reliably by adaptively balancing exploration and exploitation with a dynamic contrastive loss.
