Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning

no code implementations • 16 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.

BIG-bench Machine Learning reinforcement-learning +1

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

no code implementations • 30 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.

BIG-bench Machine Learning Reinforcement Learning (RL)

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

no code implementations • 30 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 Reinforcement Learning (RL)

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.

Descriptive Link Prediction