Search Results for author: Peter Krafft

Found 4 papers, 0 papers with code

Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning

no code implementations16 Feb 2019 Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Anirudh Goyal, Peter Krafft, Esteban Moro, Alex Pentland

A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel.

reinforcement-learning

How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology

no code implementations30 Nov 2018 Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Peter Krafft, Esteban Moro, Alex `Sandy' Pentland

This is an important problem because a common technique to improve speed and robustness of learning in deep reinforcement learning and many other machine learning algorithms is to run multiple learning agents in parallel.

reinforcement-learning

Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies

no code implementations30 Nov 2017 Dhaval Adjodah, Dan Calacci, Yan Leng, Peter Krafft, Esteban Moro, Alex Pentland

We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning.

reinforcement-learning

Topic-Partitioned Multinetwork Embeddings

no code implementations NeurIPS 2012 Peter Krafft, Juston Moore, Bruce Desmarais, Hanna M. Wallach

We introduce a joint model of network content and context designed for exploratory analysis of email networks via visualization of topic-specific communication patterns.

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