Multi-agent reinforcement learning: A selective overview of theories and algorithms
Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …
has registered tremendous success in solving various sequential decision-making problems …
Decentralized multi-agent reinforcement learning with networked agents: Recent advances
Multi-agent reinforcement learning (MARL) has long been a significant research topic in
both machine learning and control systems. Recent development of (single-agent) deep …
both machine learning and control systems. Recent development of (single-agent) deep …
Independent policy gradient for large-scale markov potential games: Sharper rates, function approximation, and game-agnostic convergence
We examine global non-asymptotic convergence properties of policy gradient methods for
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …
A potential game approach to distributed operational optimization for microgrid energy management with renewable energy and demand response
J Zeng, Q Wang, J Liu, J Chen… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In view of Internet of Energy, advanced operational optimization in microgrid energy
management system (MEMS) is expected to be scalable to accommodate various …
management system (MEMS) is expected to be scalable to accommodate various …
Gradient play in stochastic games: stationary points, convergence, and sample complexity
We study the performance of the gradient play algorithm for stochastic games (SGs), where
each agent tries to maximize its own total discounted reward by making decisions …
each agent tries to maximize its own total discounted reward by making decisions …
Dynamic cluster formation game for attributed graph clustering
Besides the topological structure, there are additional information, ie, node attributes, on top
of the plain graphs. Usually, these systems can be well modeled by attributed graphs, where …
of the plain graphs. Usually, these systems can be well modeled by attributed graphs, where …
Distributed potential ilqr: Scalable game-theoretic trajectory planning for multi-agent interactions
In this work, we develop a scalable, local tra-jectory optimization algorithm that enables
robots to interact with other robots. It has been shown that agents' interactions can be …
robots to interact with other robots. It has been shown that agents' interactions can be …
On the global convergence rates of decentralized softmax gradient play in markov potential games
Softmax policy gradient is a popular algorithm for policy optimization in single-agent
reinforcement learning, particularly since projection is not needed for each gradient update …
reinforcement learning, particularly since projection is not needed for each gradient update …
Solution sets for inverse non-cooperative linear-quadratic differential games
This letter addresses the inverse problem of differential games, where the aim is to compute
cost functions which lead to observed Nash equilibrium trajectories. The solution of this …
cost functions which lead to observed Nash equilibrium trajectories. The solution of this …
Learning game-theoretic models of multiagent trajectories using implicit layers
P Geiger, CN Straehle - Proceedings of the AAAI Conference on …, 2021 - ojs.aaai.org
For prediction of interacting agents' trajectories, we propose an end-to-end trainable
architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable …
architecture that hybridizes neural nets with game-theoretic reasoning, has interpretable …