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20192022
most citedConstrained Reinforcement Learning via Dissipative Saddle Flow Dynamics

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

cs.LG20221 cited

Constrained Reinforcement Learning via Dissipative Saddle Flow Dynamics

Tianqi Zheng, Pengcheng You, Enrique Mallada

In constrained reinforcement learning (C-RL), an agent seeks to learn from the environment a policy that maximizes the expected cumulative reward while satisfying minimum requireme…

math.OC2021

A Market Mechanism for Truthful Bidding with Energy Storage

Rajni Kant Bansal, Pengcheng You, Dennice F. Gayme +1

This paper proposes a market mechanism for multi-interval electricity markets with generator and storage participants. Drawing ideas from supply function bidding, we introduce a no…

math.OC2021

Mechanism Design for Efficient Nash Equilibrium in Oligopolistic Markets

Kaiying Lin, Beibei Wang, Pengcheng You

This paper investigates the efficiency loss in social cost caused by strategic bidding behavior of individual participants in a supply-demand balancing market, and proposes a mecha…

math.OC2020

Storage Degradation Aware Economic Dispatch

R. K. Bansal, P. You, D. F. Gayme +1

In this paper, we formulate a cycling cost aware economic dispatch problem that co-optimizes generation and storage dispatch while taking into account cycle based storage degradati…

math.OC2020

Saddle Flow Dynamics: Observable Certificates and Separable Regularization

Pengcheng You, Enrique Mallada

This paper proposes a certificate, rooted in observability, for asymptotic convergence of saddle flow dynamics of convex-concave functions to a saddle point. This observable certif…

math.OC2019

The Role of Strategic Load Participants in Two-Stage Settlement Electricity Markets

Pengcheng You, Dennice F. Gayme, Enrique Mallada

Two-stage electricity market clearing is designed to maintain market efficiency under ideal conditions, e.g., perfect forecast and nonstrategic generation. This work demonstrates t…