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Found 6 papers, 1 papers with code
All AI Models are Wrong, but Some are Optimal
This is primarily because AI models are typically constructed to best fit the data, and hence to predict the most likely future rather than to enable high-performance decision-making.
Economic Model Predictive Control as a Solution to Markov Decision Processes
However, computing the optimal policy of an MDP can be very difficult due to the curse of dimensionality present in solving the underlying Bellman equations.
Learning-based MPC from Big Data Using Reinforcement Learning
The state-of-the-art learning methods use RL to improve the performance of parameterized MPC schemes.
Bridging the gap between QP-based and MPC-based RL
We propose simple tools to promote structures in the QP, pushing it to resemble a linear MPC scheme.
Extracting Strong Policies for Robotics Tasks from zero-order trajectory optimizers
Solving high-dimensional, continuous robotic tasks is a challenging optimization problem.
Sample-efficient Cross-Entropy Method for Real-time Planning
However, their sampling inefficiency prevents them from being used for real-time planning and control.
Model-based Reinforcement Learning
reinforcement-learning
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