Wendelin Böhmer

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University of Oxford ∙ Berlin Institute of Technology (Technische Universität Berlin) ∙ Delft University of Technology

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UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

This paper focuses on cooperative value-based multi-agent reinforcement ...
10/06/2020 ∙ by Tarun Gupta, et al. ∙ 4 ∙ share

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AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning

Real world multi-agent tasks often involve varying types and quantities ...
06/07/2020 ∙ by Shariq Iqbal, et al. ∙ 9 ∙ share

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Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control

Deep multi-agent reinforcement learning (MARL) holds the promise of auto...
03/14/2020 ∙ by Christian Schroeder de Witt, et al. ∙ 13 ∙ share

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Optimistic Exploration even with a Pessimistic Initialisation

Optimistic initialisation is an effective strategy for efficient explora...
02/26/2020 ∙ by Tabish Rashid, et al. ∙ 5 ∙ share

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Deep Coordination Graphs

This paper introduces the deep coordination graph (DCG) for collaborativ...
09/27/2019 ∙ by Wendelin Böhmer, et al. ∙ 54 ∙ share

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Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning

This paper investigates the use of intrinsic reward to guide exploration...
06/05/2019 ∙ by Wendelin Böhmer, et al. ∙ 14 ∙ share

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Multitask Soft Option Learning

We present Multitask Soft Option Learning (MSOL), a hierarchical multita...
04/01/2019 ∙ by Maximilian Igl, et al. ∙ 98 ∙ share

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Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning

This paper investigates a type of instability that is linked to the gree...
12/22/2016 ∙ by Wendelin Böhmer, et al. ∙ 0 ∙ share

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Regression with Linear Factored Functions

Many applications that use empirically estimated functions face a curse ...
12/19/2014 ∙ by Wendelin Böhmer, et al. ∙ 0 ∙ share

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Robot Navigation using Reinforcement Learning and Slow Feature Analysis

The application of reinforcement learning algorithms onto real life prob...
05/04/2012 ∙ by Wendelin Böhmer, et al. ∙ 0 ∙ share

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