Investigation Of Independent Reinforcement Learning Algorithms In Multi-agent Environments

Abstract

Independent reinforcement learning algorithms have no theoretical guarantees for finding the best policy in multi-agent settings. However, in practice, prior works have reported good performance with independent algorithms in some domains and bad performance in others. Moreover, a comprehensive study of the strengths and weaknesses of independent algorithms is lacking in the literature. In this paper, we carry out an empirical comparison of the performance of independent algorithms on four PettingZoo environments that span the three main categories of multi-agent environments, i.e., cooperative, competitive, and mixed. We show that in fully-observable environments, independent algorithms can perform on par with multi-agent algorithms in cooperative and competitive settings. For the mixed environments, we show that agents trained via independent algorithms learn to perform well individually, but fail to learn to cooperate with allies and compete with enemies. We also show that adding re

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keylee2021investigation

Related papers

• Individual Specialization In Multi-task Environments With Multiagent Reinforcement Learners (2019)0.00
• Learning From Multiple Independent Advisors In Multi-agent Reinforcement Learning (2023)0.00
• Multi-agent Actor-critic For Mixed Cooperative-competitive Environments (2017)0.00
• Benchmarking Multi-agent Deep Reinforcement Learning Algorithms In Cooperative Tasks (2020)0.00
• Independent Learning In Stochastic Games (2021)6.77
• Fully Decentralized Cooperative Multi-agent Reinforcement Learning: A Survey (2024)0.00
• Multi-agent Reinforcement Learning: A Report On Challenges And Approaches (2018)0.00
• Is Independent Learning All You Need In The Starcraft Multi-agent Challenge? (2020)0.00