PaIR-Drive runs IL and RL in parallel branches with a tree-structured sampler to reach 91.2 PDMS and 87.9 EPDMS on NAVSIM benchmarks while outperforming sequential RL fine-tuning and correcting some human errors.
Amisha Bhaskar, Zahiruddin Mahammad, Sachin R Jadhav, and Pratap Tokekar
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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.
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Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving
PaIR-Drive runs IL and RL in parallel branches with a tree-structured sampler to reach 91.2 PDMS and 87.9 EPDMS on NAVSIM benchmarks while outperforming sequential RL fine-tuning and correcting some human errors.
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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.