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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 …
The role of baselines in policy gradient optimization
We study the effect of baselines in on-policy stochastic policy gradient optimization, and
close the gap between the theory and practice of policy optimization methods. Our first …
close the gap between the theory and practice of policy optimization methods. Our first …
Decentralized cooperative reinforcement learning with hierarchical information structure
Multi-agent reinforcement learning (MARL) problems are challenging due to information
asymmetry. To overcome this challenge, existing methods often require high level of …
asymmetry. To overcome this challenge, existing methods often require high level of …
Independent natural policy gradient methods for potential games: Finite-time global convergence with entropy regularization
A major challenge in multi-agent systems is that the system complexity grows dramatically
with the number of agents as well as the size of their action spaces, which is typical in real …
with the number of agents as well as the size of their action spaces, which is typical in real …
Context-aware Bayesian network actor-critic methods for cooperative multi-agent reinforcement learning
Executing actions in a correlated manner is a common strategy for human coordination that
often leads to better cooperation, which is also potentially beneficial for cooperative multi …
often leads to better cooperation, which is also potentially beneficial for cooperative multi …
[PDF][PDF] PROVABLE REINFORCEMENT LEARNING FOR CONSTRAINED AND MULTI-AGENT CONTROL SYSTEMS
D Ding - 2022 - viterbi-web.usc.edu
Reinforcement Learning (RL) is an algorithmic paradigm for sequential decision-making in
which a controller (or an agent) aims to maximize the task-associated long-term reward by …
which a controller (or an agent) aims to maximize the task-associated long-term reward by …