A new marginalized reparameterization estimator allows low-variance training of mixture policies in entropy-regularized actor-critic algorithms, matching or exceeding Gaussian policy performance in several continuous control benchmarks.
Monte carlo gradient estimation in machine learning
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Graph State-Space Models jointly learn state-space dynamics and latent relational graphs end-to-end from time series for forecasting and structure extraction.
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Revisiting Mixture Policies in Entropy-Regularized Actor-Critic
A new marginalized reparameterization estimator allows low-variance training of mixture policies in entropy-regularized actor-critic algorithms, matching or exceeding Gaussian policy performance in several continuous control benchmarks.
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Graph State-Space Models and Latent Relational Inference
Graph State-Space Models jointly learn state-space dynamics and latent relational graphs end-to-end from time series for forecasting and structure extraction.