Value-decomposition networks for cooperative multi-agent learning
P Sunehag, G Lever, A Gruslys, WM Czarnecki… - ar**_for_knowledge-based_multi-objective_multi-agent_reinforcement_learning/links/5cf8e03592851c4dd02c518a/Reward-sha**-for-knowledge-based-multi-objective-multi-agent-reinforcement-learning.pdf" data-clk="hl=it&sa=T&oi=gga&ct=gga&cd=1&d=13785574548084914414&ei=1TytZ87EA5bO6rQP6tvC0A0" data-clk-atid="7nhEKwMzUL8J" target="_blank">[PDF] researchgate.net
Reward sha** for knowledge-based multi-objective multi-agent reinforcement learning
The majority of multi-agent reinforcement learning (MARL) implementations aim to optimize
systems with respect to a single objective, despite the fact that many real-world problems are …
systems with respect to a single objective, despite the fact that many real-world problems are …
Decentralized counterfactual value with threat detection for multi-agent reinforcement learning in mixed cooperative and competitive environments
This paper proposes a fully decentralized approach to address the challenge of general
mixed cooperation and competition within the domain of Multi-Agent Reinforcement …
mixed cooperation and competition within the domain of Multi-Agent Reinforcement …
Learning Aligned Local Evaluations For Better Credit Assignment In Cooperative Coevolution
Cooperative coevolutionary algorithms prove effective in solving tasks that can be easily
decoupled into subproblems. When applied to problems with high coupling (where the …
decoupled into subproblems. When applied to problems with high coupling (where the …
Leveraging Fitness Critics To Learn Robust Teamwork
Co-evolutionary algorithms have successfully trained agent teams for tasks such as
autonomous exploration or robot soccer. However generally, such approaches seek a single …
autonomous exploration or robot soccer. However generally, such approaches seek a single …
Toll-based reinforcement learning for efficient equilibria in route choice
The problem of traffic congestion incurs numerous social and economical repercussions and
has thus become a central issue in every major city in the world. For this work we look at the …
has thus become a central issue in every major city in the world. For this work we look at the …
Fitness sha** for multiple teams
Coevolutionary algorithms have effectively trained multiagent teams to collectively solve
complex problems. However, in many real-world applications, changes to the environment …
complex problems. However, in many real-world applications, changes to the environment …
Maedys: Multiagent evolution via dynamic skill selection
Evolving effective coordination strategies in tightly coupled multi-agent settings with sparse
team fitness evaluations is challenging. It relies on multiple agents simultaneously stumbling …
team fitness evaluations is challenging. It relies on multiple agents simultaneously stumbling …
Learning Individual Potential-Based Rewards in Multi-Agent Reinforcement Learning
C Yang, P Xu, J Zhang - IEEE Transactions on Games, 2024 - ieeexplore.ieee.org
A great challenge for applying multi-agent reinforcement learning (MARL) in the field of
game AI is to enable agents to learn diversified policies to handle different gamespecific …
game AI is to enable agents to learn diversified policies to handle different gamespecific …
Decoupled Monte Carlo Tree Search for Cooperative Multi-Agent Planning
The number of agents exponentially increases the complexity of a cooperative multi-agent
planning problem. Decoupled planning is one of the viable approaches to reduce this …
planning problem. Decoupled planning is one of the viable approaches to reduce this …