Publications

Peer-to-Peer Energy Trading and Energy Conversion in Interconnected Multi-Energy Microgrids Using Multi-Agent Deep Reinforcement Learning

Student Member Ieee Tianyi Chen

Shengrong Bu

Ieee Xue Liu Member

Ieee Jikun Kang Fellow

Fellow Ieee F. Richard Yu

Fellow Ieee. Zhu Han

A key aspect of multi-energy microgrids (MEMGs) is the capability to efficiently convert and store energy in order to reduce the costs and e… (see more)nvironmental impact. Peer-to-peer (P2P) energy trading is a novel paradigm for decentralised energy market designs. In this paper, we investigate the external P2P energy trading problem and internal energy conversion problem within interconnected residential, commercial and industrial MEMGs. These two problems are complex decision-making problems with enormous high-dimensional data and uncertainty, so a multi-agent deep reinforcement learning approach combining the multi-agent actor-critic algorithm with the twin delayed deep deterministic policy gradient algorithm is proposed. The proposed approach can handle the high-dimensional continuous action space and aligns with the nature of P2P energy trading with multiple MEMGs. Simulation results based on three real-world MG datasets show that the proposed approach significantly reduces each MG’s average hourly operation cost. The impact of carbon tax pricing is also considered.

2022-01-01

IEEE Transactions on Smart Grid (published)

doi.org

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

Eric Larsen

Sébastien Lachapelle

This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a method… (see more)ology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The problem we address occurs in the context of two-stage stochastic programming, where the second stage is demanding computationally. We aim to predict at a high speed the expected TDOS associated with the second-stage problem, conditionally on the first-stage variables. This may be used in support of the solution to the overall two-stage problem by avoiding the online generation of multiple second-stage scenarios and solutions. We formulate the tactical prediction problem as a stochastic optimal prediction program, whose solution we approximate with supervised machine learning. The training data set consists of a large number of deterministic operational problems generated by controlled probabilistic sampling. The labels are computed based on solutions to these problems (solved independently and offline), employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application on load planning for rail transportation show that deep learning models produce accurate predictions in very short computing time (milliseconds or less). The predictive accuracy is close to the lower bounds calculated based on sample average approximation of the stochastic prediction programs.

2022-01-01

INFORMS Journal on Computing (published)

doi.org

arxiv.org

Probabilistic surrogate networks for simulators with unbounded randomness

Andreas Munk

Berend Zwartsenberg

Adam Ścibior

Atilim Güneş Baydin

Andrew Lawrence Stewart

Goran Fernlund

Anoush Poursartip

Frank Wood

We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike… (see more) traditional approaches to surrogate modeling, our surrogates retain the interpretable structure and control flow of the reference simulator. Our surrogates target stochastic simulators where the number of random variables itself can be stochastic and potentially unbounded. Our framework further enables an automatic replacement of the reference simulator with the surrogate when undertaking amortized inference. The fidelity and speed of our surrogates allow for both faster stochastic simulation and accurate and substantially faster posterior inference. Using an illustrative yet non-trivial example we show our surrogates' ability to accurately model a probabilistic program with an unbounded number of random variables. We then proceed with an example that shows our surrogates are able to accurately model a complex structure like an unbounded stack in a program synthesis example. We further demonstrate how our surrogate modeling technique makes amortized inference in complex black-box simulators an order of magnitude faster. Specifically, we do simulator-based materials quality testing, inferring safety-critical latent internal temperature profiles of composite materials undergoing curing.

2022-01-01

UAI (published)

proceedings.mlr.press

openreview.net

Question Personalization in an Intelligent Tutoring System

Sabina Elkins

Robert Belfer

Ekaterina Kochmar

Iulian V. Serban

Jackie Cheung

2022-01-01

AIED (2) (published)

doi.org

arxiv.org

Realistic Evaluation of Transductive Few-Shot Learning - Supplementary Material

Olivier Veilleux

Éts Montréal

Malik Boudiaf

Pablo Piantanida

Ismail Ben

Ayed Éts Montreal

In the main tables of the paper, we did not include the performances of α-TIM in the standard balanced setting. Here, we emphasize that α-… (see more)TIM is a generalization of TIM [1] as when α → 1 (i.e., the α-entropies tend to the Shannon entropies), α-TIM tends to TIM. Therefore, in the standard setting, where optimal hyper-parameter α is obtained over validation tasks that are balanced (as in the standard validation tasks of the original TIM and the other existing methods), the performance of α-TIM is the same as TIM. When α is tuned on balanced validation tasks, we obtain an optimal value of α very close to 1, and our α-mutual information approaches the standard mutual information. When the validation tasks are uniformly random, as in our new setting and in the validation plots we provided in the main figure, one can see that the performance of α-TIM remains competitive when we tend to balanced testing tasks (i.e., when a is increasing), but is significantly better than TIM when we tend to uniformly-random testing tasks (a = 1). These results illustrate the flexibility of α-divergences, and are in line with the technical analysis provided in the main paper.

2022-01-01

(published)

www.semanticscholar.org

Recipe for a General, Powerful, Scalable Graph Transformer

Ladislav Rampášek

Mikhail Galkin

Vijay Prakash Dwivedi

Anh Tuan Luu

Guy Wolf

Dominique Beaini

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art result… (see more)s on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publications but they lack a common foundation about what constitutes a good positional or structural encoding, and what differentiates them. In this paper, we summarize the different types of encodings with a clearer definition and categorize them as being

openreview.net

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

Rishabh Agarwal

Max Schwarzer

Pablo Samuel Castro

Aaron Courville

Marc Gendron-Bellemare

openreview.net

Robust Policy Learning over Multiple Uncertainty Sets

Annie Xie

Shagun Sodhani

Chelsea Finn

Joelle Pineau

Amy Zhang

Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments. While system identification methods prov… (see more)ide a way to infer the variation from online experience, they can fail in settings where fast identification is not possible. Another dominant approach is robust RL which produces a policy that can handle worst-case scenarios, but these methods are generally designed to achieve robustness to a single uncertainty set that must be specified at train time. Towards a more general solution, we formulate the multi-set robustness problem to learn a policy robust to different perturbation sets. We then design an algorithm that enjoys the benefits of both system identification and robust RL: it reduces uncertainty where possible given a few interactions, but can still act robustly with respect to the remaining uncertainty. On a diverse set of control tasks, our approach demonstrates improved worst-case performance on new environments compared to prior methods based on system identification and on robust RL alone.

2022-01-01

ICML (published)

proceedings.mlr.press

arxiv.org

Robustness of Whittle Index Policy to Model Approximation

Amit Sinha

Aditya Mahajan

2022-01-01

Social Science Research Network (published)

doi.org

Scalable Operator Allocation for Multirobot Assistance: A Restless Bandit Approach

Abhinav Dahiya

Nima Akbarzadeh

Aditya Mahajan

Stephen L. Smith

In this article, we consider the problem of allocating human operators in a system with multiple semiautonomous robots. Each robot is requir… (see more)ed to perform an independent sequence of tasks, subject to a chance of failing and getting stuck in a fault state at every task. If and when required, a human operator can assist or teleoperate a robot. Conventional dynamic programming-based techniques used to solve such problems face scalability issues due to an exponential growth of state and action spaces with the number of robots and operators. In this article, we derive conditions under which the operator allocation problem satisfies a technical condition called indexability, thereby enabling the use of the Whittle index heuristic. The conditions are easy to check, and we show that they hold for a wide range of problems of interest. Our key insight is to leverage the structure of the value function of individual robots, resulting in conditions that can be verified separately for each state of each robot. We apply these conditions to two types of transitions commonly seen in remote robot supervision systems. Through numerical simulations, we demonstrate the efficacy of Whittle index policy as a near-optimal and scalable approach that outperforms existing scalable methods.

2022-01-01

IEEE Transactions on Control of Network Systems (published)

doi.org

arxiv.org

Scaling the Number of Tasks in Continual Learning

Timothee LESORT

Oleksiy Ostapenko

Diganta Misra

Md Rifat Arefin

Pau Rodriguez

Laurent Charlin

Irina Rish