RLA-WM predicts residual latent actions via flow matching to create visual feature world models that outperform prior feature-based and diffusion approaches while enabling offline video-based robot RL.
Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor
4 Pith papers cite this work. Polarity classification is still indexing.
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Q-chunking improves offline-to-online RL sample efficiency on long-horizon sparse-reward manipulation tasks by applying action chunking to TD learning.
Koopman-assisted RL reformulates max-entropy algorithms using controlled Koopman tensors and reports SOTA performance versus neural SAC on Lorenz, fluid flow, and other systems.
IDQL generalizes IQL into an actor-critic framework and uses diffusion policies for robust policy extraction, outperforming prior offline RL methods.
citing papers explorer
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Learning Visual Feature-Based World Models via Residual Latent Action
RLA-WM predicts residual latent actions via flow matching to create visual feature world models that outperform prior feature-based and diffusion approaches while enabling offline video-based robot RL.
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Reinforcement Learning with Action Chunking
Q-chunking improves offline-to-online RL sample efficiency on long-horizon sparse-reward manipulation tasks by applying action chunking to TD learning.
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Koopman-Assisted Reinforcement Learning
Koopman-assisted RL reformulates max-entropy algorithms using controlled Koopman tensors and reports SOTA performance versus neural SAC on Lorenz, fluid flow, and other systems.
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IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
IDQL generalizes IQL into an actor-critic framework and uses diffusion policies for robust policy extraction, outperforming prior offline RL methods.