Search Results for author:
Found 8 papers, 4 papers with code
Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design
Multi-cellular robot design aims to create robots comprised of numerous cells that can be efficiently controlled to perform diverse tasks.
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.
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).
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).
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).
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
