Wendelin Böhmer | DeepAI

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∙ 07/30/2023

You Shall not Pass: the Zero-Gradient Problem in Predict and Optimize for Convex Optimization

Predict and optimize is an increasingly popular decision-making paradigm...

0 Grigorii Veviurko, et al. ∙

∙ 06/12/2023

Diverse Projection Ensembles for Distributional Reinforcement Learning

In contrast to classical reinforcement learning, distributional reinforc...

0 Moritz A. Zanger, et al. ∙

∙ 06/09/2023

The Role of Diverse Replay for Generalisation in Reinforcement Learning

In reinforcement learning (RL), key components of many algorithms are th...

0 Max Weltevrede, et al. ∙

∙ 12/06/2022

Active Classification of Moving Targets with Learned Control Policies

In this paper, we consider the problem where a drone has to collect sema...

0 Álvaro Serra-Gómez, et al. ∙

∙ 10/21/2022

Planning with Uncertainty: Deep Exploration in Model-Based Reinforcement Learning

Deep model-based Reinforcement Learning (RL) has shown super-human perfo...

0 Yaniv Oren, et al. ∙

∙ 10/06/2020

UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning

This paper focuses on cooperative value-based multi-agent reinforcement ...

4 Tarun Gupta, et al. ∙

∙ 06/07/2020

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

Real world multi-agent tasks often involve varying types and quantities ...

9 Shariq Iqbal, et al. ∙

∙ 03/14/2020

Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control

Deep multi-agent reinforcement learning (MARL) holds the promise of auto...

13 Christian Schroeder de Witt, et al. ∙

∙ 02/26/2020

Optimistic Exploration even with a Pessimistic Initialisation

Optimistic initialisation is an effective strategy for efficient explora...

5 Tabish Rashid, et al. ∙

∙ 09/27/2019

Deep Coordination Graphs

This paper introduces the deep coordination graph (DCG) for collaborativ...

54 Wendelin Böhmer, et al. ∙

∙ 06/05/2019

Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning

This paper investigates the use of intrinsic reward to guide exploration...

14 Wendelin Böhmer, et al. ∙

∙ 04/01/2019

Multitask Soft Option Learning

We present Multitask Soft Option Learning (MSOL), a hierarchical multita...

98 Maximilian Igl, et al. ∙

∙ 12/22/2016

Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning

This paper investigates a type of instability that is linked to the gree...

0 Wendelin Böhmer, et al. ∙

∙ 12/19/2014

Regression with Linear Factored Functions

Many applications that use empirically estimated functions face a curse ...

0 Wendelin Böhmer, et al. ∙

∙ 05/04/2012

Robot Navigation using Reinforcement Learning and Slow Feature Analysis

The application of reinforcement learning algorithms onto real life prob...

0 Wendelin Böhmer, et al. ∙

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