180 citations · 1.5k across the 49 of their papers we have counts for
13 papers · 1 filter
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi +4
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algo…
SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions
Chengwei Zhang, Xiaohong Li, Jianye Hao +5
Although many reinforcement learning methods have been proposed for learning the optimal solutions in single-agent continuous-action domains, multiagent coordination domains with c…
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Gregory Palmer, Rahul Savani, Karl Tuyls
In Multi-Agent Reinforcement Learning (MA-RL), independent cooperative learners must overcome a number of pathologies to learn optimal joint policies. Addressing one pathology ofte…
A Comparative Study of Bug Algorithms for Robot Navigation
Kimberly McGuire, Guido de Croon, Karl Tuyls
This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these meth…
Fast Convergence for Object Detection by Learning how to Combine Error Functions
Benjamin Schnieders, Karl Tuyls
In this paper, we introduce an innovative method to improve the convergence speed and accuracy of object detection neural networks. Our approach, CONVERGE-FAST-AUXNET, is based on…
Relational Deep Reinforcement Learning
Vinicius Zambaldi, David Raposo, Adam Santoro +13
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through st…