Regularizing Action Policies for Smooth Control with Reinforcement Learning

• URL: http://arxiv.org/abs/2012.06644v1
• Date: Fri, 11 Dec 2020 21:35:24 GMT
• Title: Regularizing Action Policies for Smooth Control with Reinforcement Learning
• Authors: Siddharth Mysore, Bassel Mabsout, Renato Mancuso, Kate Saenko
• Abstract summary: Conditioning for Action Policy Smoothness (CAPS) is an effective yet intuitive regularization on action policies. CAPS offers consistent improvement in the smoothness of the learned state-to-action mappings of neural network controllers. Tested on a real system, improvements in controller smoothness on a quadrotor drone resulted in an almost 80% reduction in power consumption.
• Score: 47.312768123967025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies. This trend often presents itself in the form of control signal oscillation and can result in poor control, high power consumption, and undue system wear. We introduce Conditioning for Action Policy Smoothness (CAPS), an effective yet intuitive regularization on action policies, which offers consistent improvement in the smoothness of the learned state-to-action mappings of neural network controllers, reflected in the elimination of high-frequency components in the control signal. Tested on a real system, improvements in controller smoothness on a quadrotor drone resulted in an almost 80% reduction in power consumption while consistently training flight-worthy controllers. Project website: http://ai.bu.edu/caps

Related papers

• Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution [51.83951489847344]
  In robotics applications, smooth control signals are commonly preferred to reduce system wear and energy efficiency. In this work, we aim to bridge this performance gap by growing discrete action spaces from coarse to fine control resolution. Our work indicates that an adaptive control resolution in combination with value decomposition yields simple critic-only algorithms that yield surprisingly strong performance on continuous control tasks.
  arXiv  Detail & Related papers  (2024-04-05T17:58:37Z)
• Deployable Reinforcement Learning with Variable Control Rate [14.838483990647697]
  We propose a variant of Reinforcement Learning (RL) with variable control rate. In this approach, the policy decides the action the agent should take as well as the duration of the time step associated with that action. We show the efficacy of SEAC through a proof-of-concept simulation driving an agent with Newtonian kinematics.
  arXiv  Detail & Related papers  (2024-01-17T15:40:11Z)
• Real-Time Model-Free Deep Reinforcement Learning for Force Control of a Series Elastic Actuator [56.11574814802912]
  State-of-the art robotic applications utilize series elastic actuators (SEAs) with closed-loop force control to achieve complex tasks such as walking, lifting, and manipulation. Model-free PID control methods are more prone to instability due to nonlinearities in the SEA. Deep reinforcement learning has proved to be an effective model-free method for continuous control tasks.
  arXiv  Detail & Related papers  (2023-04-11T00:51:47Z)
• Improving the Performance of Robust Control through Event-Triggered Learning [74.57758188038375]
  We propose an event-triggered learning algorithm that decides when to learn in the face of uncertainty in the LQR problem. We demonstrate improved performance over a robust controller baseline in a numerical example.
  arXiv  Detail & Related papers  (2022-07-28T17:36:37Z)
• Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control [0.9023847175654602]
  Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications. Initiative action state augmentation (IASA) for actor-critic-based RL controllers is introduced. This augmentation does not require any expert knowledge, leaving the approach model free.
  arXiv  Detail & Related papers  (2022-01-31T16:29:19Z)
• Adversarially Regularized Policy Learning Guided by Trajectory Optimization [31.122262331980153]
  We propose adVErsarially Regularized pOlicy learNIng guided by trajeCtory optimizAtion (VERONICA) for learning smooth control policies. Our proposed approach improves the sample efficiency of neural policy learning and enhances the robustness of the policy against various types of disturbances.
  arXiv  Detail & Related papers  (2021-09-16T00:02:11Z)
• Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty [22.276925045008788]
  emphglsrpl offers a formulation to improve existing controllers with reinforcement learning (RL) We show superior performance of our approach on a contact-rich peg-insertion task under position and orientation uncertainty.
  arXiv  Detail & Related papers  (2021-06-08T13:06:35Z)
• Lyapunov-Regularized Reinforcement Learning for Power System Transient Stability [5.634825161148484]
  This paper proposes a Lyapunov regularized RL approach for optimal frequency control for transient stability in lossy networks. Case study shows that introducing the Lyapunov regularization enables the controller to be stabilizing and achieve smaller losses.
  arXiv  Detail & Related papers  (2021-03-05T18:55:26Z)
• Enforcing robust control guarantees within neural network policies [76.00287474159973]
  We propose a generic nonlinear control policy class, parameterized by neural networks, that enforces the same provable robustness criteria as robust control. We demonstrate the power of this approach on several domains, improving in average-case performance over existing robust control methods and in worst-case stability over (non-robust) deep RL methods.
  arXiv  Detail & Related papers  (2020-11-16T17:14:59Z)
• Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion [95.1825179206694]
  We present a framework that synthesizes robust controllers for a quadruped robot. A high-level controller learns to choose from a set of primitives in response to changes in the environment. A low-level controller that utilizes an established control method to robustly execute the primitives.
  arXiv  Detail & Related papers  (2020-09-21T16:49:26Z)

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.