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Variance-Averse $n$-Step Offline Reinforcement Learning for Sparse Long-Horizon Environments
Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions. However, this expressiveness also exposes a key challenge in heterogeneous datasets: generative policies can reproduce unreliable action modes whose r
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