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Found 3 papers, 1 papers with code
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
The physics engine augmented with the residual model is then used to control the marble in the maze environment using a model-predictive feedback over a receding horizon.
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
We believe that stronger feature propagation together with larger networks (and thus larger search space) allows RL agents to learn more complex functions of states and thus improves the sample efficiency.
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
Our experiments show that our RL agent trained with a reference path outperformed a model-free PID controller of the type commonly used on many robotic platforms for trajectory tracking.
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