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Found 7 papers, 1 papers with code
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games
Under a set of assumptions, we prove the convergence of the algorithm to a subjective notion of Markov perfect Nash equilibrium in NAMGs.
A Consensus-Based Generalized Multi-Population Aggregative Game with Application to Charging Coordination of Electric Vehicles
In the upper layer, population coordinators collaborate for a distributed estimation of the coupling aggregate term in the agents' cost function and the associated Lagrange multiplier of the coupling constraint, transmitting the latest updated values to their population's agents.
Generalized Stochastic Dynamic Aggregative Game for Demand-Side Management in Microgrids with Shared Battery
In this paper, we focus on modeling and analysis of demand-side management in a microgrid where agents utilize grid energy and a shared battery charged by renewable energy sources.
Learning Robust Model Predictive Control for Voltage Control of Islanded Microgrid
This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of {nonlinear} loads and parametric uncertainties at the primary level of control.
Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game
In this paper, we propose a continuous RL-based DDA methodology for a visual working memory (VWM) game to handle the complex search space for the difficulty of memorization.
A Tractable Truthful Profit Maximization Mechanism Design with Autonomous Agents
Task allocation is a crucial process in modern systems, but it is often challenged by incomplete information about the utilities of participating agents.
A Sufficient Condition to Guarantee Non-Simultaneous Charging and Discharging of Household Battery Energy Storage
In this letter, we model the day-ahead price-based demand response of a residential household with battery energy storage and other controllable loads, as a convex optimization problem.
