Publications related to Markov decision process

Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots

Alcherio Martinoli, Chiara Ercolani, Faezeh Rahbar

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly refe ...

MDPI2023

Optimal Combination Policies For Adaptive Social Learning

Virginia Bordignon, Ping Hu, Stefan Vlaski

This paper investigates the effect of combination policies on the performance of adaptive social learning in non-stationary environments. By analyzing the relation between the error probability and the underlying graph topology, we prove that in the slow a ...

IEEE2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees

Giancarlo Ferrari Trecate, Luca Furieri, Baiwei Guo, Andrea Martin

How can we synthesize a safe and near-optimal control policy for a partially-observed system, if all we are given is one historical input/output trajectory that has been corrupted by noise? To address this challenge, we suggest a novel data-driven controll ...

2021

Exploring policy change through agent-based simulation

Raphaël Klein

Policymaking is a complex process that has been studied using policy process theories almost exclusively. These theories have been built using a large number of qualitative cases. Such methods are useful for theory building but remain limited for theory ex ...

EPFL2021

Decision Learning and Adaptation Over Multi-Task Networks

Ali H. Sayed

This paper studies the operation of multi-agent networks engaged in multi-task decision problems under the paradigm of simultaneous learning and adaptation. Two scenarios are considered:one in which a decision must be taken among multiple states of nature ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Sylvain Calinon, Amirreza Razmjoo Fard, Teguh Santoso Lembono

paper studies exploiting action-level learning (imitation) in the optimal control problem context. Cost functions defined by the optimal control methods are similar to the goal-level learning (emulation) in animals. However, imitating the robot's or others ...

IEEE2021

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

Daniel Kuhn, Napat Rujeerapaiboon, Kilian Schindler, Wolfram Wiesemann

Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout and the steady growth in photovoltaic capacity. The reduced profitability of peak/off- peak arbitrage forces hydropower producers to reco ...

2021

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

Volkan Cevher, Luca Viano, Yu-Ting Huang, Kamalaruban Parameswaran

We study the inverse reinforcement learning (IRL) problem under a transition dynamics mismatch between the expert and the learner. Specifically, we consider the Maximum Causal Entropy (MCE) IRL learner model and provide a tight upper bound on the learner’s ...

2021

Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

Colin Neil Jones

A stochastic model predictive control framework over unreliable Bernoulli communication channels, in the presence of unbounded process noise and under bounded control inputs, is presented for tracking a reference signal. The data losses in the control chan ...

2021

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Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots

Optimal Combination Policies For Adaptive Social Learning

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees

Exploring policy change through agent-based simulation

Decision Learning and Adaptation Over Multi-Task Networks

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots

Exploring policy change through agent-based simulation

Optimal Combination Policies For Adaptive Social Learning

Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees

Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch

Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

Decision Learning and Adaptation Over Multi-Task Networks