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.
Probabilis- tic mixture-of-experts for efficient deep reinforcement learning.arXiv preprint arXiv:2104.09122,
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MoMa QL uses MMD moment matching to enforce distribution-level convergence of conditional score functions in flow-based RL policies for improved sampling efficiency.
PRISM-WM uses a context-aware MoE with latent orthogonalization to model hybrid dynamics and reduce rollout drift for model-based planning.
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Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems
PRISM-WM uses a context-aware MoE with latent orthogonalization to model hybrid dynamics and reduce rollout drift for model-based planning.