Maximum Likelihood Constraint Inference from Stochastic Demonstrations

• URL: http://arxiv.org/abs/2102.12554v1
• Date: Wed, 24 Feb 2021 20:46:55 GMT
• Title: Maximum Likelihood Constraint Inference from Stochastic Demonstrations
• Authors: David L. McPherson, Kaylene C. Stocking, S. Shankar Sastry
• Abstract summary: This paper extends maximum likelihood constraint inference to applications by using maximum causal entropy likelihoods. We propose an efficient algorithm that computes constraint likelihood and risk tolerance in a unified Bellman backup.
• Score: 5.254702845143088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demonstrations can be computed, given the system dynamics and task objective, and the maximum likelihood constraints can be identified. Prior constraint inference work has focused mainly on deterministic models. Stochastic models, however, can capture the uncertainty and risk tolerance that are often present in real systems of interest. This paper extends maximum likelihood constraint inference to stochastic applications by using maximum causal entropy likelihoods. Furthermore, we propose an efficient algorithm that computes constraint likelihood and risk tolerance in a unified Bellman backup, allowing us to generalize to stochastic systems without increasing computational complexity.

Related papers

• Accelerated zero-order SGD under high-order smoothness and overparameterized regime [79.85163929026146]
  We present a novel gradient-free algorithm to solve convex optimization problems. Such problems are encountered in medicine, physics, and machine learning. We provide convergence guarantees for the proposed algorithm under both types of noise.
  arXiv  Detail & Related papers  (2024-11-21T10:26:17Z)
• Learning Controlled Stochastic Differential Equations [61.82896036131116]
  This work proposes a novel method for estimating both drift and diffusion coefficients of continuous, multidimensional, nonlinear controlled differential equations with non-uniform diffusion. We provide strong theoretical guarantees, including finite-sample bounds for (L2), (Linfty), and risk metrics, with learning rates adaptive to coefficients' regularity. Our method is available as an open-source Python library.
  arXiv  Detail & Related papers  (2024-11-04T11:09:58Z)
• Probabilistic Flux Limiters [0.873811641236639]
  A popular method to virtually eliminate Gibbs oscillations in under-resolved simulations is to use a flux limiter. Here, we introduce a conceptually distinct type of flux limiter that is designed to handle the effects of randomness in the model. We show that a machine learned, probabilistic flux limiter may be used in a shock capturing code to more accurately capture shock profiles.
  arXiv  Detail & Related papers  (2024-05-13T21:06:53Z)
• Correct-by-Construction Control for Stochastic and Uncertain Dynamical Models via Formal Abstractions [44.99833362998488]
  We develop an abstraction framework that can be used to solve this problem under various modeling assumptions. We use state-of-the-art verification techniques to compute an optimal policy on the iMDP with guarantees for satisfying the given specification. We then show that, by construction, we can refine this policy into a feedback controller for which these guarantees carry over to the dynamical model.
  arXiv  Detail & Related papers  (2023-11-16T11:03:54Z)
• Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
  We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences. Our method is especially suitable for problems with well-specified likelihoods. We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
  arXiv  Detail & Related papers  (2023-11-08T00:10:21Z)
• Online Constraint Tightening in Stochastic Model Predictive Control: A Regression Approach [49.056933332667114]
  No analytical solutions exist for chance-constrained optimal control problems. We propose a data-driven approach for learning the constraint-tightening parameters online during control. Our approach yields constraint-tightening parameters that tightly satisfy the chance constraints.
  arXiv  Detail & Related papers  (2023-10-04T16:22:02Z)
• Probabilistic Exponential Integrators [36.98314810594263]
  Like standard solvers, they suffer performance penalties for certain stiff systems. This paper develops a class of probabilistic exponential solvers with favorable properties. We evaluate the proposed methods on multiple stiff differential equations.
  arXiv  Detail & Related papers  (2023-05-24T10:13:13Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning [16.019477271828745]
  We consider the challenge of finding a deterministic policy for a Markov decision process. This class of problem is known to be hard, but the combined requirements of determinism and uniform optimality can create learning instability. We present a suitable constrained reinforcement learning algorithm that prevents learning instability.
  arXiv  Detail & Related papers  (2022-01-20T02:33:24Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems [7.6146285961466]
  This paper offers a data-based, probablistic perspective that enables the quantification of predictive uncertainties. We formulate the coarse-graining process by employing a probabilistic state-space model. It is capable of reconstructing the evolution of the full, fine-scale system.
  arXiv  Detail & Related papers  (2019-12-30T16:07:46Z)

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.