Search Results for author:
Found 22 papers, 11 papers with code
Social Environment Design
Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making.
Multi-Sender Persuasion -- A Computational Perspective
The core solution concept here is the Nash equilibrium of senders' signaling policies.
Never Explore Repeatedly in Multi-Agent Reinforcement Learning
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Symmetry-Aware Robot Design with Structured Subgroups
Robot design aims at learning to create robots that can be easily controlled and perform tasks efficiently.
Low-Rank Modular Reinforcement Learning via Muscle Synergy
Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator.
Non-Linear Coordination Graphs
Value decomposition multi-agent reinforcement learning methods learn the global value function as a mixing of each agent's individual utility functions.
Multi-Agent Policy Transfer via Task Relationship Modeling
We demonstrate that the task representation can capture the relationship among tasks, and can generalize to unseen tasks.
Self-Organized Polynomial-Time Coordination Graphs
However, one critical challenge in this paradigm is the complexity of greedy action selection with respect to the factorized values.
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning tasks put a high demand on the volume of training samples.
Learning Homophilic Incentives in Sequential Social Dilemmas
Promoting cooperation among self-interested agents is a long-standing and interdisciplinary problem, but receives less attention in multi-agent reinforcement learning (MARL).
Context-Aware Sparse Deep Coordination Graphs
Learning sparse coordination graphs adaptive to the coordination dynamics among agents is a long-standing problem in cooperative multi-agent learning.
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL
We propose a novel learning framework to encourage homophilic incentives and show that it achieves stable cooperation in both SSDs of public goods and tragedy of the commons.
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
RODE: Learning Roles to Decompose Multi-Agent Tasks
Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces.
Off-Policy Multi-Agent Decomposed Policy Gradients
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
Incorporating Pragmatic Reasoning Communication into Emergent Language
Emergentism and pragmatics are two research fields that study the dynamics of linguistic communication along substantially different timescales and intelligence levels.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+2
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
In this paper, we synergize these two paradigms and propose a role-oriented MARL framework (ROMA).
Multiagent Systems
Influence-Based Multi-Agent Exploration
We present two exploration methods: exploration via information-theoretic influence (EITI) and exploration via decision-theoretic influence (EDTI), by exploiting the role of interaction in coordinated behaviors of agents.
Learning Nearly Decomposable Value Functions Via Communication Minimization
Recently, value function factorization learning emerges as a promising way to address these challenges in collaborative multi-agent systems.
Convergence of Multi-Agent Learning with a Finite Step Size in General-Sum Games
Learning in a multi-agent system is challenging because agents are simultaneously learning and the environment is not stationary, undermining convergence guarantees.
Contrast and visual saliency similarity-induced index for assessing image quality
Contrast is an inherent visual attribute that indicates image quality, and visual saliency (VS) is a quality that attracts the attention of human beings.
