Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control

• URL: http://arxiv.org/abs/2206.10524v1
• Date: Tue, 21 Jun 2022 16:49:09 GMT
• Title: Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control
• Authors: Katie Kang, Paula Gradu, Jason Choi, Michael Janner, Claire Tomlin, Sergey Levine
• Abstract summary: We seek a mechanism to constrain the agent to states and actions that resemble those that it was trained on. In control theory, Lyapunov stability and control-invariant sets allow us to make guarantees about controllers. density models allow us to estimate the training data distribution.
• Score: 64.61499213110334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learned models and policies can generalize effectively when evaluated within the distribution of the training data, but can produce unpredictable and erroneous outputs on out-of-distribution inputs. In order to avoid distribution shift when deploying learning-based control algorithms, we seek a mechanism to constrain the agent to states and actions that resemble those that it was trained on. In control theory, Lyapunov stability and control-invariant sets allow us to make guarantees about controllers that stabilize the system around specific states, while in machine learning, density models allow us to estimate the training data distribution. Can we combine these two concepts, producing learning-based control algorithms that constrain the system to in-distribution states using only in-distribution actions? In this work, we propose to do this by combining concepts from Lyapunov stability and density estimation, introducing Lyapunov density models: a generalization of control Lyapunov functions and density models that provides guarantees on an agent's ability to stay in-distribution over its entire trajectory.

Related papers

• Adding Conditional Control to Diffusion Models with Reinforcement Learning [59.295203871547336]
  Diffusion models are powerful generative models that allow for precise control over the characteristics of the generated samples. This work presents a novel method based on reinforcement learning (RL) to add additional controls, leveraging an offline dataset.
  arXiv  Detail & Related papers  (2024-06-17T22:00:26Z)
• Distributionally Robust Policy and Lyapunov-Certificate Learning [13.38077406934971]
  Key challenge in designing controllers with stability guarantees for uncertain systems is the accurate determination of and adaptation to shifts in model parametric uncertainty during online deployment. We tackle this with a novel distributionally robust formulation of the Lyapunov derivative chance constraint ensuring a monotonic decrease of the Lyapunov certificate. We show that, for the resulting closed-loop system, the global stability of its equilibrium can be certified with high confidence, even with Out-of-Distribution uncertainties.
  arXiv  Detail & Related papers  (2024-04-03T18:57:54Z)
• Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression [3.405252606286664]
  This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. For continuous enhancement in predictive performance, a data-efficient online learning strategy with a decentralized event-triggered mechanism is proposed. To demonstrate the efficacy of the proposed learning-based controller, a comparative analysis is conducted, contrasting it with both conventional distributed control laws and offline learning methodologies.
  arXiv  Detail & Related papers  (2024-02-05T16:41:17Z)
• Delta-AI: Local objectives for amortized inference in sparse graphical models [64.5938437823851]
  We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs) Our approach is based on the observation that when the sampling of variables in a PGM is seen as a sequence of actions taken by an agent, sparsity of the PGM enables local credit assignment in the agent's policy learning objective. We illustrate $Delta$-AI's effectiveness for sampling from synthetic PGMs and training latent variable models with sparse factor structure.
  arXiv  Detail & Related papers  (2023-10-03T20:37:03Z)
• Distributionally Robust Post-hoc Classifiers under Prior Shifts [31.237674771958165]
  We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors. We present an extremely lightweight post-hoc approach that performs scaling adjustments to predictions from a pre-trained model.
  arXiv  Detail & Related papers  (2023-09-16T00:54:57Z)
• A Coupled Flow Approach to Imitation Learning [24.024918837659474]
  In reinforcement learning and imitation learning, an object of central importance is the state distribution induced by the policy. In this work, we investigate applications of a normalizing flow-based model for the aforementioned distributions. Our algorithm, Coupled Flow Learning (CFIL), achieves state-of-the-art performance on benchmark tasks with a single expert trajectory.
  arXiv  Detail & Related papers  (2023-04-29T17:10:17Z)
• Learnable Distribution Calibration for Few-Shot Class-Incremental Learning [122.2241120474278]
  Few-shot class-incremental learning (FSCIL) faces challenges of memorizing old class distributions and estimating new class distributions given few training samples. We propose a learnable distribution calibration (LDC) approach, with the aim to systematically solve these two challenges using a unified framework.
  arXiv  Detail & Related papers  (2022-10-01T09:40:26Z)
• 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)
• Implicit Distributional Reinforcement Learning [61.166030238490634]
  implicit distributional actor-critic (IDAC) built on two deep generator networks (DGNs) Semi-implicit actor (SIA) powered by a flexible policy distribution. We observe IDAC outperforms state-of-the-art algorithms on representative OpenAI Gym environments.
  arXiv  Detail & Related papers  (2020-07-13T02:52:18Z)
• How Training Data Impacts Performance in Learning-based Control [67.7875109298865]
  This paper derives an analytical relationship between the density of the training data and the control performance. We formulate a quality measure for the data set, which we refer to as $rho$-gap. We show how the $rho$-gap can be applied to a feedback linearizing control law.
  arXiv  Detail & Related papers  (2020-05-25T12:13:49Z)

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.