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.
Lillicrap, Jonathan J
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AdaGamma stabilizes state-dependent discounting in deep actor-critic RL by adding a return-consistency regularizer, delivering gains on continuous-control benchmarks and a real-world logistics A/B test.
Introduces an off-policy adversarial imitation learning method with double Q stabilization that reduces samples required to match expert behavior.
citing papers explorer
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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.
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AdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning
AdaGamma stabilizes state-dependent discounting in deep actor-critic RL by adding a return-consistency regularizer, delivering gains on continuous-control benchmarks and a real-world logistics A/B test.
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Enabling Off-Policy Imitation Learning with Deep Actor Critic Stabilization
Introduces an off-policy adversarial imitation learning method with double Q stabilization that reduces samples required to match expert behavior.