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Accelerate Reinforcement Learning with PID Controllers in the Pendulum Simulations

arXiv 2210.00770 ↗First posted 3 Oct 2022eess.SY cs.LG cs.SYcited by 0

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Abstract (arXiv)

We propose a Proportional Integral Derivative (PID) controller-based coaching scheme to expedite reinforcement learning (RL).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 3 Oct 2022.

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16 of 28 references.

References · 16

  1. 2016OpenAI Gymcited by 1,736
  2. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  3. 2018Learning Dexterous In-Hand Manipulationcited by 687
  4. 2019Deep Equilibrium Modelscited by 615
  5. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  6. 2017Deep learning for universal linear embeddings of nonlinear dynamicscited by 494
  7. 2019Augmented Neural ODEscited by 397
  8. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 251
  9. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 151
  10. 2020Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Predictioncited by 138
  11. 2020Reinforcement Learning via Fenchel-Rockafellar Dualitycited by 100
  12. 2020Deep Drone Acrobaticscited by 75
4 more
  1. 2020Actor-Critic Reinforcement Learning for Control with Stability Guaranteecited by 45
  2. 2019RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstrationcited by 19
  3. 2018Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learningcited by 17
  4. 2018One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RLcited by 11

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