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Scalable Multi-Agent Reinforcement Learning with General Utilities
The objective is to find a localized policy that maximizes the average of the team's local utility functions without the full observability of each agent in the team.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Policy-based Primal-Dual Methods for Convex Constrained Markov Decision Processes
We study convex Constrained Markov Decision Processes (CMDPs) in which the objective is concave and the constraints are convex in the state-action occupancy measure.
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization
We study entropy-regularized constrained Markov decision processes (CMDPs) under the soft-max parameterization, in which an agent aims to maximize the entropy-regularized value function while satisfying constraints on the expected total utility.
