arXiv research

MPC outperforms reactive budgeting in non-stationary return environments.

Model learns and plans in real-time under constraints for robotic systems.

We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose AdaSearch, a general algor…

Efficient learning-based MPC for unknown nonlinear systems with state constraints.

We examine optimal execution models that take into account both market microstructure impact and informational costs. Informational footprint is related to order flow and is represented by the trader's influence on the flow imbalance process, while microstructure influence is captured by instantaneous price impact. We …

A new approach optimizes weights in DLP for better risk-adjusted performance.

In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narrativ…

In a previous paper we obtained formulae for the volume of a causal diamond or Alexandrov open set $I^+(p) \cap I^-(q)$ whose duration $τ(p,q)$ is short compared with the curvature scale. In the present paper we obtain asymptotic formulae valid when the point $q$ recedes to the future boundary ${\cal I}^+$ of an asymp…

Time dilation $\frac{1}{\sqrt{1-v^2}}$ and relative velocity $v$ are observationally indistinguishable in the special theory of relativity, a duality that carries over into the general theory under Fermi coordinates along a curve (in coordinate-independent language, in the tangent Minkowski space along the curve). For …

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of unc…

Sample-efficient exploration is crucial not only for discovering rewarding experiences but also for adapting to environment changes in a task-agnostic fashion. A principled treatment of the problem of optimal input synthesis for system identification is provided within the framework of sequential Bayesian experimental …

Derives time-averaged active inference from control principles.

A new ML algorithm solves complex economic control problems.

Study optimal liquidation strategies with infinite horizon and regime switching.

Action-bisimulation learns long-horizon controllability for reinforcement learning.

New model predicts optimal control for restless bandit problems.

Reward tweaking optimizes behavior for long-term goals by adjusting the reward function.

Solves infinite horizon portfolio problem with path-dependent labor income.

Study optimal portfolios in a non-Markovian regime-switching model with random time horizon.

Logarithmic regret achieved in continuous-time linear-quadratic reinforcement learning.

Derives optimal control conditions using calculus of variations.

Study optimal healthcare spending under Epstein-Zin preferences for longevity.

HiDe learns hierarchical control for complex tasks by separating planning and control.

We study a stochastic, continuous time model on a finite horizon for a firm that produces a single good. We model the production capacity as an Ito diffusion controlled by a nondecreasing process representing the cumulative investment. The firm aims to maximize its expected total net profit by choosing the optimal inve…

New control theory shows neural networks can be sparsely active over time.

Extends RL to random stopping times, improving optimization.

In this paper, we investigate dynamic optimization problems featuring both stochastic control and optimal stopping in a finite time horizon. The paper aims to develop new methodologies, which are significantly different from those of mixed dynamic optimal control and stopping problems in the existing literature, to stu…

Financial event studies often misestimate causal effects due to misspecified factor models.

Study compares nine deep learning architectures for multi-horizon financial forecasting.

Framework uses expert intervention to solve long-horizon reinforcement learning tasks.

We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems. Our key innovation is an auxiliary variable construction coupled with an inverse Hessian approximation computed using a receding history of iterates and gradients. It is th…

Proposes a recursive MPC scheme with probabilistic safety guarantees for uncertain dynamic systems.

Study cost-driven state representation learning for control from partial observations.

Study optimal consumption with drawdown limits over a fixed time frame.

Behavior cloning can achieve horizon-independent sample complexity in offline imitation learning.

A novel text-independent speaker identification (SI) method is proposed. This method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic information among adjacent frames as feature sets to capture speaker's characteristics. In order to utilize dynamic information, we design super-MFCCs features by cas…

New approach finds solutions to games with unbounded controls.

Optimizes Ethena's yield strategy by controlling stETH and ETH futures positions.

New bounds for prediction with experts using geometric stopping.

Study learns state representations from observations for control, proving guarantees.

The paper optimizes portfolios in a financial market with correlated assets using a stochastic volatility model.

Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of …

Investors pay for additional asset information based on utility maximization.

New method solves stochastic control problems with delays using deep learning.

The paper analyzes optimal overbetting strategies for a satellite investment account.

This paper studies a class of continuous-time scalar-state stochastic Linear-Quadratic (LQ) optimal control problem with the linear control constraints. Applying the state separation theorem induced from its special structure, we develop the explicit solution for this class of problem. The revealed optimal control poli…

Deep Galerkin Method estimates value function for mean-field control problem.

This paper challenges the conventional wisdom of trend-following by showing that the medium-term horizon adds little value once short- and long-term components are included.