Search · four archives
Search · four archives
25 papers · ranked by Valyu relevance
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb + 2 more
'Igor Mordatch'] We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as…
Pablo Hernández-Leal, Bilal Kartal, Matthew E. Taylor
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in…
James Orr, Ayan Dutta, David Cheneler, Stephen Monk
Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the…
Pamul Yadav, Ashutosh Mishra, Shiho Kim, Ching-Yao Chan + 3 more
Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning…
Ardi Tampuu, Tambet Matiisen, Dorian Kodelja, Ilya Kuzovkin + 5 more
'Kristjan Korjus' 'Juhan Aru' 'Jaan Aru' 'Raul Vicente' 'Cheng-Yi Xia'] Evolution of cooperation and competition can appear when multiple adaptive agents share a biological, social, or technological niche. In the present work we study how cooperation and competition emerge between autonomous agents that learn by…
Borja Fernandez-Gauna, Ismael Etxeberria-Agiriano, Manuel Graña, Catalin Buiu
'Catalin Buiu'] Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round…
Afshin Oroojlooyjadid, Davood Hajinezhad
Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. In this review article, we have focused on presenting recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. In particular, we have focused on five common approaches on modeling and solving cooperative…
Jacopo Castellini, Sam Devlin, Frans A. Oliehoek, Rahul Savani
Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent’s contribution to the overall performance, which is crucial for learning…
Yutong Wang, Mehul Damani, Pamela Wang, Yuhong Cao + 1 more
'Guillaume Sartoretti'] Purpose of review: Recent advances in sensing, actuation, and computation have opened the door to multi-robot systems consisting of hundreds/thousands of robots, with promising applications to automated manufacturing, disaster relief, harvesting, last-mile delivery, port/airport operations, or…
Raphael Köster, Edgar A. Duéñez-Guzmán, William A. Cunningham, Joel Z. Leibo
'Joel Z. Leibo'] Title: Significance The emergence of group bias has a long history in social psychology, including the positing of innate biases. Experimental evidence is fundamentally limited because every brain is both a product of experience and evolution. With cognitive models, one can control both the cognitive…
Wolfram Barfuss
A dynamical systems perspective on multi-agent learning, based on the link between evolutionary game theory and reinforcement learning, provides an improved, qualitative understanding of the emerging collective learning dynamics. However, confusion exists with respect to how this dynamical systems account of…
Anushka Deshpande
The aim of this paper is twofold. First, it seeks to uncover the algorithms that humans and other animals employ for learning in decision-making strategies within non-zero-sum games, specifically focusing on fully observable iterated prisoner’s dilemma scenarios. Second, it aims to develop a new model to explain…
Kaiyue Wu, Xiao‐Jun Zeng
It can largely benefit the reinforcement learning (RL) process of each agent if multiple geographically distributed agents perform their separate RL tasks cooperatively. Different from multi-agent reinforcement learning (MARL) where multiple agents are in a common environment and should learn to cooperate or compete…
Diego E. Kleiman, Diwakar Shukla
Machine Learning is increasingly applied to improve the efficiency and accuracy of Molecular Dynamics (MD) simulations. Although the growth of distributed computer clusters has allowed researchers to obtain higher amounts of data, unbiased MD simulations have difficulty sampling rare states, even under massively…
Yusi Chen, Angela Radulescu, Herbert Zheng Wu
Understanding the intentions and beliefs of others, a phenomenon known as “theory of mind”, is a crucial element in social behavior. These beliefs and perceptions are inherently subjective and latent, making them often unobservable for investigation. Social interactions further complicate the matter, as multiple agents…
Arvin Tashakori
- Healthcare: which includes constantly vital signs monitoring of severely ill patients and elderly care, and may act depending on the situation. For example, suppose a patient with diabetes type I needs constant blood sugar monitoring [3]. - Remote sensing, monitoring: various applications from industrial level…
Wei-Chen Liao, Ti-Rong Wu, I-Chen Wu
Multi-agent reinforcement Learning (MARL) is often challenged by the sight range dilemma, where agents either receive insufficient or excessive information from their environment. In this paper, we propose a novel method, called Dynamic Sight Range Selection (DSR), to address this issue. DSR utilizes an Upper…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Clémence Bergerot, Pawel Romanczuk, Wolfram Barfuss
Understanding how cognition shapes behavior across contexts remains a fundamental challenge for many disciplines. In particular, for the optimism heuristic–i.e., the tendency to overweight positive (relative to negative) information–knowledge remains fragmented, with models developed in specific domains in isolation.…
Augustin Chartouny, Mehdi Khamassi, Benoît Girard
Unlike humans, who continuously adapt to both known and unknown situations, most reinforcement learning agents struggle to adjust to changing environments. In this paper, we present a new model-based reinforcement learning method that adapts to local task changes online. This method can detect changes at the level of…
Authors not listed
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Authors not listed
Inverse molecular design aims to generate novel chemical structures that satisfy multiple property constraints, yet reinforcement-learning (RL) fine-tuning can be sensitive to how objectives are converted into a scalar reward. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape…
Authors not listed
Computational methods for generating molecules with specific physiochemical properties or biolog- ical activity can greatly assist drug discovery efforts. Deep learning generative models constitute a significant step towards that direction. In this work, we introduce a novel approach that utilizes a Reinforcement…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter + 4 more
Reinforcement learning (RL) is a powerful paradigm that has gained popularity across multiple domains. However, applying RL may come at a cost of multiple interactions between the agent and the environment. This cost can be especially pronounced when the single feedback from the environment is slow or computationally…