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Tom Danino

dblp:347/6678 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Reinforcement learning · 72% Efficient and distributed learning · 28%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
distributed training		 0.712023
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning · NeurIPS 2023
Machine learning › Reinforcement learning › multi-agent reinforcement learning
experience sharing		 0.712023
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning · NeurIPS 2023
Machine learning › Reinforcement learning
multi-agent reinforcement learning		 0.712023
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning · NeurIPS 2023
Machine learning › Reinforcement learning › deep reinforcement learning
deep q-network		 0.212023
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning · NeurIPS 2023
Machine learning › Reinforcement learning
value-based reinforcement learning		 0.212023
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning · NeurIPS 2023

Methods — techniques the papers use, named apart from their topics

prioritized experience replay · 0.7experience sharing · 0.7
YearPublicationVenuePosition
2023 Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
abstract
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training. The intuition behind this is that even a small number of relevant experiences from other agents could help each agent learn. Unlike many other multi-agent RL algorithms, this approach allows for largely decentralized training, requiring only a limited communication channel between agents. We show that our approach outperforms baseline no-sharing decentralized training and state-of-the art multi-agent RL algorithms. Further, sharing only a small number of highly relevant experiences outperforms sharing all experiences between agents, and the performance uplift from selective experience sharing is robust across a range of hyperparameters and DQN variants.
Matthias Gerstgrasser, Tom Danino, Sarah Keren
 NeurIPS2