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Found 6 papers, 1 papers with code
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
This method significantly improves learning by reducing the sample complexity such that the dataset only needs to have sufficient coverage of the stochastic states.
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a cumulative constraint on the expected utility (cost).
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems
The proposed algorithm is distributed in two aspects: (i) the learned policy is a distributed policy that maps a local state of an agent to its local action and (ii) the learning/training is distributed, during which each agent updates its policy based on its own and neighbors' information.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processes
This paper presents the first model-free, simulator-free reinforcement learning algorithm for Constrained Markov Decision Processes (CMDPs) with sublinear regret and zero constraint violation.
FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
In this paper, we propose a new type of Actor, named forward-looking Actor or FORK for short, for Actor-Critic algorithms.
QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection
The algorithm consists of an offline machine learning algorithm for learning the probabilistic information spreading model and an online optimal stopping algorithm to detect misinformation.
