PO-MPC unifies prior MPPI-based RL approaches under a single KL-regularized framework that uses the planner distribution as a prior, with new variations yielding performance gains in experiments.
We inherit all architectural choices from TD-MPC2
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A KL-regularization Framework for Learning to Plan with Adaptive Priors
PO-MPC unifies prior MPPI-based RL approaches under a single KL-regularized framework that uses the planner distribution as a prior, with new variations yielding performance gains in experiments.