
Learning Safe MultiAgent Control with Decentralized Neural Barrier Certificates
We study the multiagent safe control problem where agents should avoid ...
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DerivativeFree Policy Optimization for RiskSensitive and Robust Control Design: Implicit Regularization and Sample Complexity
Direct policy search serves as one of the workhorses in modern reinforce...
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Asynchronous Advantage Actor Critic: Nonasymptotic Analysis and Linear Speedup
Asynchronous and parallel implementation of standard reinforcement learn...
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NearOptimal Regret Bounds for ModelFree RL in NonStationary Episodic MDPs
We consider modelfree reinforcement learning (RL) in nonstationary Mar...
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Reinforcement Learning in NonStationary DiscreteTime LinearQuadratic MeanField Games
In this paper, we study large population multiagent reinforcement learn...
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ModelBased MultiAgent RL in ZeroSum Markov Games with NearOptimal Sample Complexity
Modelbased reinforcement learning (RL), which finds an optimal policy u...
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POLYHOOT: MonteCarlo Planning in Continuous Space MDPs with NonAsymptotic Analysis
MonteCarlo planning, as exemplified by MonteCarlo Tree Search (MCTS), ...
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Information State Embedding in Partially Observable Cooperative MultiAgent Reinforcement Learning
Multiagent reinforcement learning (MARL) under partial observability ha...
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Approximate Equilibrium Computation for DiscreteTime LinearQuadratic MeanField Games
While the topic of meanfield games (MFGs) has a relatively long history...
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Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks
This paper proposes a fully asynchronous scheme for policy evaluation of...
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Decentralized MultiAgent Reinforcement Learning with Networked Agents: Recent Advances
Multiagent reinforcement learning (MARL) has long been a significant an...
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MultiAgent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Recent years have witnessed significant advances in reinforcement learni...
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NonCooperative Inverse Reinforcement Learning
Making decisions in the presence of a strategic opponent requires one to...
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Policy Optimization for H_2 Linear Control with H_∞ Robustness Guarantee: Implicit Regularization and Global Convergence
Policy optimization (PO) is a key ingredient for reinforcement learning ...
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Online Planning for Decentralized Stochastic Control with Partial History Sharing
In decentralized stochastic control, standard approaches for sequential ...
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Stochastic Convergence Results for Regularized ActorCritic Methods
In this paper, we present a stochastic convergence proof, under suitable...
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A CommunicationEfficient MultiAgent ActorCritic Algorithm for Distributed Reinforcement Learning
This paper considers a distributed reinforcement learning problem in whi...
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Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies
Policy gradient (PG) methods are a widely used reinforcement learning me...
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Policy Optimization Provably Converges to Nash Equilibria in ZeroSum Linear Quadratic Games
We study the global convergence of policy optimization for finding the N...
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A MultiAgent OffPolicy ActorCritic Algorithm for Distributed Reinforcement Learning
This paper extends offpolicy reinforcement learning to the multiagent ...
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CommunicationEfficient Distributed Reinforcement Learning
This paper studies the distributed reinforcement learning (DRL) problem ...
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FiniteSample Analyses for Fully Decentralized MultiAgent Reinforcement Learning
Despite the increasing interest in multiagent reinforcement learning (M...
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Distributed Learning of Average Belief Over Networks Using Sequential Observations
This paper addresses the problem of distributed learning of average beli...
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Fully Decentralized MultiAgent Reinforcement Learning with Networked Agents
We consider the problem of fully decentralized multiagent reinforcement...
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