C-DSAC applies the Cramér distance to distributional value learning inside SAC and outperforms standard SAC on robotic benchmarks, with larger gains in complex environments due to confidence-driven conservative updates.
Addressing function ap- proximation error in actor-critic methods
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Distributional Reinforcement Learning via the Cram\'er Distance
C-DSAC applies the Cramér distance to distributional value learning inside SAC and outperforms standard SAC on robotic benchmarks, with larger gains in complex environments due to confidence-driven conservative updates.