An adaptive RL-MPC framework uses RL to inform MPPI sampling and aggregates MPPI samples for value estimation, delivering up to 72% higher success rates and 2.1x faster convergence on tasks like race driving and Lunar Lander with obstacles.
Actor-critic model predictive control: Differentiable optimization meets reinforcement learn- ing
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DreamerV3 enables pixel-to-control policies for drone racing that reach 9 m/s in both simulation and real hardware-in-the-loop tests.
citing papers explorer
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Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making
An adaptive RL-MPC framework uses RL to inform MPPI sampling and aggregates MPPI samples for value estimation, delivering up to 72% higher success rates and 2.1x faster convergence on tasks like race driving and Lunar Lander with obstacles.
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Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight
DreamerV3 enables pixel-to-control policies for drone racing that reach 9 m/s in both simulation and real hardware-in-the-loop tests.