mlsanityData to 14 Oct 2026

Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty

arXiv 2404.04699 ↗First posted 6 Apr 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

This work describes a technique for active rejection of multiple independent and time-correlated stochastic disturbances for a nonlinear flexible inverted pendulum with cart system with uncertain model parameters. The control law is determined through deep reinforcement learning, specifically with a continuous actor-critic variant of deep Q-learning known as Deep Deterministic Policy Gradient, while the disturbance magnitudes evolve via independent stochastic processes. Simulation results are then compared with those from a classical control system.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 9 references that are in the map. First posted on arXiv 6 Apr 2024.

Builds on

2 of 9 references.

References · 2

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2016OpenAI Gymcited by 1,736

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