Search Results - "Cassano, Lucas"

Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates by Cassano, Lucas, Yuan, Kun, Sayed, Ali H.

“…This article develops a fully decentralized multiagent algorithm for policy evaluation. The proposed scheme can be applied to two distinct scenarios. In the…”
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Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels by Bucarelli, Maria Sofia, Cassano, Lucas, Siciliano, Federico, Mantrach, Amin, Silvestri, Fabrizio

“…In practical settings, classification datasets are obtained through a labelling process that is usually done by humans. Labels can be noisy as they are…”
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Team Policy Learning for Multi-agent Reinforcement Learning by Cassano, Lucas, Alghunaim, Sulaiman A., Sayed, Ali H.

“…This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on…”
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Teamwork and Exploration in Reinforcement Learning by Cassano, Lucas Cesar Eduardo

“…Reinforcement learning (RL) is a powerful machine learning paradigm that studies the interaction between a single agent with an unknown environment. A plethora…”
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Logical Team Q-learning: An approach towards factored policies in cooperative MARL by Cassano, Lucas, Sayed, Ali H

“…We address the challenge of learning factored policies in cooperative MARL scenarios. In particular, we consider the situation in which a team of agents…”
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Distributed Value-Function Learning with Linear Convergence Rates by Cassano, Lucas, Yuan, Kun, Sayed, Ali H.

“…In this paper we develop a fully decentralized algorithm for policy evaluation with off-policy learning and linear function approximation. The proposed…”
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ISL: A novel approach for deep exploration by Cassano, Lucas, Sayed, Ali H

“…In this article we explore an alternative approach to address deep exploration and we introduce the ISL algorithm, which is efficient at performing deep…”
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Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates by Cassano, Lucas, Yuan, Kun, Sayed, Ali H

“…This work develops a fully decentralized multi-agent algorithm for policy evaluation. The proposed scheme can be applied to two distinct scenarios. In the…”
Get full text