Multiple Plans are Better than One: Diverse Stochastic Planning

• URL: http://arxiv.org/abs/2012.15485v1
• Date: Thu, 31 Dec 2020 07:29:11 GMT
• Title: Multiple Plans are Better than One: Diverse Stochastic Planning
• Authors: Mahsa Ghasemi, Evan Scope Crafts, Bo Zhao, Ufuk Topcu
• Abstract summary: In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. We formulate a problem, called diverse planning, that aims to generate a set of representative behaviors that are near-optimal.
• Score: 26.887796946596243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative -- small and diverse -- behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems.

Related papers

• Optimization-Driven Adaptive Experimentation [7.948144726705323]
  Real-world experiments involve batched & delayed feedback, non-stationarity, multiple objectives & constraints, and (often some) personalization. Tailoring adaptive methods to address these challenges on a per-problem basis is infeasible, and static designs remain the de facto standard. We present a mathematical programming formulation that can flexibly incorporate a wide range of objectives, constraints, and statistical procedures.
  arXiv  Detail & Related papers  (2024-08-08T16:29:09Z)
• Differentiation of Multi-objective Data-driven Decision Pipeline [34.577809430781144]
  Real-world scenarios frequently involve multi-objective data-driven optimization problems. Traditional two-stage methods apply a machine learning model to estimate problem coefficients, followed by invoking a solver to tackle the predicted optimization problem. Recent efforts have focused on end-to-end training of predictive models that use decision loss derived from the downstream optimization problem.
  arXiv  Detail & Related papers  (2024-06-02T15:42:03Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty [56.30846158280031]
  Task planning for embodied AI has been one of the most challenging problems. We propose a task-agnostic method named 'planning as in-painting' The proposed framework achieves promising performances in various embodied AI tasks.
  arXiv  Detail & Related papers  (2023-12-02T10:07:17Z)
• Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints [76.84999501420938]
  We introduce a conceptual and computational framework for assessing how the choice of target affects individuals' outcomes. We show that the level of multiplicity that stems from target variable choice can be greater than that stemming from nearly-optimal models of a single target.
  arXiv  Detail & Related papers  (2023-06-23T18:57:14Z)
• Motion Planning by Learning the Solution Manifold in Trajectory Optimization [6.127237810365965]
  We present an optimization method that learns to generate an infinite set of solutions for motion planning problems. Results indicate that the experimental model represents an infinite set of homotopic solutions for motion planning problems.
  arXiv  Detail & Related papers  (2021-07-13T04:47:47Z)
• Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning [52.74071439183113]
  We study the predict-then-optimize framework in the context of sequential decision problems (formulated as MDPs) solved via reinforcement learning. Two significant computational challenges arise in applying decision-focused learning to MDPs.
  arXiv  Detail & Related papers  (2021-06-06T23:53:31Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation [101.22379613810881]
  We consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process. We propose a tractable approximation that allows us to scale our method to high-capacity neural network models.
  arXiv  Detail & Related papers  (2021-02-16T06:04:27Z)
• Learning the Solution Manifold in Optimization and Its Application in Motion Planning [4.177892889752434]
  We learn manifold on the variable such as the variable such model represents an infinite set of solutions. In our framework, we reduce problem estimation by using this importance. We apply to motion-planning problems, which involve the optimization of high-dimensional parameters.
  arXiv  Detail & Related papers  (2020-07-24T08:05:36Z)
• Multi-tier Automated Planning for Adaptive Behavior (Extended Version) [0.4129225533930965]
  We propose a multi-tier framework for planning that allows the specification of different sets of assumptions. We show how to solve problem instances by a succinct compilation to a form of non-deterministic planning.
  arXiv  Detail & Related papers  (2020-02-27T21:16:01Z)

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.