Extended Radial Basis Function Controller for Reinforcement Learning

• URL: http://arxiv.org/abs/2009.05866v2
• Date: Wed, 9 Dec 2020 06:44:17 GMT
• Title: Extended Radial Basis Function Controller for Reinforcement Learning
• Authors: Nicholas Capel, Naifu Zhang
• Abstract summary: This paper proposes a hybrid reinforcement learning controller which dynamically interpolates a model-based linear controller and an arbitrary differentiable policy. The linear controller is designed based on local linearised model knowledge, and stabilises the system in a neighbourhood about an operating point. Learning has been done on both model-based (PILCO) and model-free (DDPG) frameworks.
• Score: 3.42658286826597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There have been attempts in reinforcement learning to exploit a priori knowledge about the structure of the system. This paper proposes a hybrid reinforcement learning controller which dynamically interpolates a model-based linear controller and an arbitrary differentiable policy. The linear controller is designed based on local linearised model knowledge, and stabilises the system in a neighbourhood about an operating point. The coefficients of interpolation between the two controllers are determined by a scaled distance function measuring the distance between the current state and the operating point. The overall hybrid controller is proven to maintain the stability guarantee around the neighborhood of the operating point and still possess the universal function approximation property of the arbitrary non-linear policy. Learning has been done on both model-based (PILCO) and model-free (DDPG) frameworks. Simulation experiments performed in OpenAI gym demonstrate stability and robustness of the proposed hybrid controller. This paper thus introduces a principled method allowing for the direct importing of control methodology into reinforcement learning.

Related papers

• Random Features Approximation for Control-Affine Systems [6.067043299145924]
  We propose two novel classes of nonlinear feature representations which capture control affine structure. Our methods make use of random features (RF) approximations, inheriting the expressiveness of kernel methods at a lower computational cost.
  arXiv  Detail & Related papers  (2024-06-10T17:54:57Z)
• In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States [84.24300005271185]
  We propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.
  arXiv  Detail & Related papers  (2023-01-27T22:28:19Z)
• A stabilizing reinforcement learning approach for sampled systems with partially unknown models [0.0]
  We suggest a method to guarantee practical stability of the system-controller closed loop in a purely online learning setting. To achieve the claimed results, we employ techniques of classical adaptive control. The method is tested in adaptive traction control and cruise control where it proved to significantly reduce the cost.
  arXiv  Detail & Related papers  (2022-08-31T09:20:14Z)
• Actor-Critic based Improper Reinforcement Learning [61.430513757337486]
  We consider an improper reinforcement learning setting where a learner is given $M$ base controllers for an unknown Markov decision process. We propose two algorithms: (1) a Policy Gradient-based approach; and (2) an algorithm that can switch between a simple Actor-Critic scheme and a Natural Actor-Critic scheme.
  arXiv  Detail & Related papers  (2022-07-19T05:55:02Z)
• Neural Lyapunov Differentiable Predictive Control [2.042924346801313]
  We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees. In conjunction, our approach jointly learns a Lyapunov function that certifies the regions of state-space with stable dynamics.
  arXiv  Detail & Related papers  (2022-05-22T03:52:27Z)
• Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models [16.511440197186918]
  We propose a new method to combine model-based safety with model-free reinforcement learning. We show that a low-dimensional dynamical model is sufficient to capture the dynamics of the closed-loop system. We illustrate that the found linear model is able to provide guarantees by safety-critical optimal control framework.
  arXiv  Detail & Related papers  (2022-05-11T22:03:18Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• Online Algorithms and Policies Using Adaptive and Machine Learning Approaches [0.22020053359163297]
  Two classes of nonlinear dynamic systems are considered, both of which are control-affine. We propose a combination of a Reinforcement Learning based policy in the outer loop suitably chosen to ensure stability and optimality for the nominal dynamics. In addition to establishing a stability guarantee with real-time control, the AC-RL controller is also shown to lead to parameter learning with persistent excitation.
  arXiv  Detail & Related papers  (2021-05-13T22:51:25Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)
• Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions [96.63967125746747]
  Reinforcement learning framework learns the model uncertainty present in the CBF and CLF constraints. RL-CBF-CLF-QP addresses the problem of model uncertainty in the safety constraints.
  arXiv  Detail & Related papers  (2020-04-16T10:51:33Z)
• Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework [108.4560749465701]
  The Pontryagin Differentiable Programming methodology establishes a unified framework to solve a broad class of learning and control tasks. We investigate three learning modes of the PDP: inverse reinforcement learning, system identification, and control/planning. We demonstrate the capability of the PDP in each learning mode on different high-dimensional systems, including multi-link robot arm, 6-DoF maneuvering quadrotor, and 6-DoF rocket powered landing.
  arXiv  Detail & Related papers  (2019-12-30T15:35:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.