SOM is an actor-critic algorithm that constructs the target velocity field for one-step MeanFlow policies directly from the Q-function via score estimation and probability flow ODE, achieving claimed SOTA on locomotion tasks with reduced training and inference time.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, 2018
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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.
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Score-Based One-step MeanFlow Policy Optimization
SOM is an actor-critic algorithm that constructs the target velocity field for one-step MeanFlow policies directly from the Q-function via score estimation and probability flow ODE, achieving claimed SOTA on locomotion tasks with reduced training and inference time.
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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.