Parameterized MPC exactly represents optimal policies for MDPs with future information under identified structural conditions and parameters can be learned via RL.
Mbrl-lib: A modular library for model-based reinforcement learning
4 Pith papers cite this work, alongside 15 external citations. Polarity classification is still indexing.
years
2026 4verdicts
UNVERDICTED 4representative citing papers
PECTS learns dynamics and CBFs to constrain MPC trajectories probabilistically, enabling safer RL in stochastic unknown environments via sampling-based optimization.
The paper presents stable-worldmodel (swm), a platform with high-performance data layer, modern world model baselines, planning solvers, and extended environments for reproducible research and generalization evaluation.
Hybrid RL-PID controllers track angle of attack better and show greater robustness than PID alone within a defined operational envelope for re-entry attitude control.
citing papers explorer
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Solving Markov Decision Processes with Future Information via MPC
Parameterized MPC exactly represents optimal policies for MDPs with future information under identified structural conditions and parameters can be learned via RL.
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A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning
PECTS learns dynamics and CBFs to constrain MPC trajectories probabilistically, enabling safer RL in stochastic unknown environments via sampling-based optimization.
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stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
The paper presents stable-worldmodel (swm), a platform with high-performance data layer, modern world model baselines, planning solvers, and extended environments for reproducible research and generalization evaluation.
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Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry
Hybrid RL-PID controllers track angle of attack better and show greater robustness than PID alone within a defined operational envelope for re-entry attitude control.