A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study motion planning for points avoiding obstacles in a plane.
Paper analyzes neural network complexity for planning problems.
CoMPNetX uses neural networks to efficiently solve constrained motion planning problems.
The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level …
The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We …
Neural A* uses machine learning to improve path planning efficiency.
Study experiment planning with function approximation in contextual bandit problems.
We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces. Compared to more classical sampling-based methods like RRT, our approach achieves much bet…
Motion planning is an essential component in most of today's robotic applications. In this work, we consider the learning setting, where a set of solved motion planning problems is used to improve the efficiency of motion planning on different, yet similar problems. This setting is important in applications with rapidl…
New method designs fairer transport plans with uncertainty.
This paper argues, first, that a major problem in the planning of large infrastructure projects is the high level of misinformation about costs and benefits that decision makers face in deciding whether to build, and the high risks such misinformation generates. Second, it explores the causes of misinformation and risk…
Improves RL planning by proposing sub-goals hierarchically.
Proposes a framework for automated radiation therapy treatment planning with uncertainty quantification.
Deep network predicts action sequences for complex tasks from a scene image.
This paper presents a unifying framework for reinforcement learning and planning.
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue…
In this paper we study a notion of topological complexity for the motion planning problem. The topological complexity is a number which measures discontinuity of the process of motion planning in the configuration space X. More precisely, it is the minimal number k such that there are k different motion planning rules,…
This paper studies when particle filtering is efficient for planning in partially observed systems.
New algorithm for planning in observable POMDPs in quasi-polynomial time.
A planning approach learns skills from interactions, balancing exploration and exploitation.
Survey of integrating planning and learning in model-based reinforcement learning.
Improved CEM for fast real-time planning in high-dimensional control tasks.
We designed a grid world task to study human planning and re-planning behavior in an unknown stochastic environment. In our grid world, participants were asked to travel from a random starting point to a random goal position while maximizing their reward. Because they were not familiar with the environment, they needed…
This work defines a complexity measure for BAMDP planning and introduces state abstraction for more efficient approximate planning.
This work clarifies the role of inference types in planning.
We consider the problem of robot motion planning in an oriented Riemannian manifold as a topological motion planning problem in its oriented frame bundle. For this purpose, we study the topological complexity of oriented frame bundles, derive an upper bound for this invariant and certain lower bounds from cup length co…
This work tackles long-term visual planning by goal-conditioned hierarchical predictors.
Conventional wisdom holds that model-based planning is a powerful approach to sequential decision-making. It is often very challenging in practice, however, because while a model can be used to evaluate a plan, it does not prescribe how to construct a plan. Here we introduce the "Imagination-based Planner", the first m…
We study control systems invariant under a Lie group with application to the problem of nonlinear trajectory planning. A theory of symmetry reduction of exterior differential systems is employed to demonstrate how symmetry reduction and reconstruction is effective in the explicit, exact construction of planned system t…
The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combination…
The paper explores how planning with models improves credit assignment in reinforcement learning.
We introduce a framework for model learning and planning in stochastic domains with continuous state and action spaces and non-Gaussian transition models. It is efficient because (1) local models are estimated only when the planner requires them; (2) the planner focuses on the most relevant states to the current planni…
Survey on manifold complexities and motion planning in robotics.
Paper tackles non-uniform coverage planning for robots.
The problem of retrosynthetic planning can be framed as one player game, in which the chemist (or a computer program) works backwards from a molecular target to simpler starting materials though a series of choices regarding which reactions to perform. This game is challenging as the combinatorial space of possible cho…
A new method for multi-agent planning on graphs outperforms existing approaches.
Motion planning and control are key problems in a collection of robotic applications including the design of autonomous agile vehicles and of minimalist manipulators. These problems can be accurately formalized within the language of affine connections and of geometric control theory. In this paper we overview recent r…
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical solutions to a given operational problem. In this context, the tactical solution is less detailed than the operational one but it has to be comput…
A novel pedestrian path-planning model using reinforcement learning.
Proposes DLGPD model to learn dynamics from images for planning.
Here we shall consider a very popular practical applied problem of managing mode switching (in this work we are considering managing billing plans). Out of the two parties (service provider and service consumer), participating in the processes modelled here, we shall consider only a consumer type of a problem. Herein w…
Framework uses RL and simulation for optimal microgrid energy storage planning.
Unified approach to path planning using probabilistic inference on factor graphs.
We consider tackling a single-agent RL problem by distributing it to learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their advice, taking the form of action values, is then communicated to an aggregator, which is in control of the system. We show that the local plan…
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective probabilistic method for learning and inference of basic movements. The motion planning problem is formulated as learning on a directed grap…
Vanguard uses AI to create personalized financial plans.
Intelligent motion planning is one of the core components in automated vehicles, which has received extensive interests. Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability. Sampling based methods cannot guarantee the optimality of the gener…
New framework guides resource usage to achieve sublinear regret in adversarial settings.