Peer Learning: Learning Complex Policies In Groups From Scratch Via Action Recommendations

Abstract

Peer learning is a novel high-level reinforcement learning framework for agents learning in groups. While standard reinforcement learning trains an individual agent in trial-and-error fashion, all on its own, peer learning addresses a related setting in which a group of agents, i.e., peers, learns to master a task simultaneously together from scratch. Peers are allowed to communicate only about their own states and actions recommended by others: "What would you do in my situation?". Our motivation is to study the learning behavior of these agents. We formalize the teacher selection process in the action advice setting as a multi-armed bandit problem and therefore highlight the need for exploration. Eventually, we analyze the learning behavior of the peers and observe their ability to rank the agents' performance within the study group and understand which agents give reliable advice. Further, we compare peer learning with single agent learning and a state-of-the-art action advice basel

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyderstroff2023peer

Related papers

• Peer Prediction For Learning Agents (2022)0.00
• Explainable Action Advising For Multi-agent Reinforcement Learning (2022)6.77
• Multi-agent Advisor Q-learning (2021)0.00
• Multi-agent Cooperation Through Learning-aware Policy Gradients (2024)0.00
• Fast Peer Adaptation With Context-aware Exploration (2024)0.00
• Learning From Multiple Independent Advisors In Multi-agent Reinforcement Learning (2023)0.00
• Policy Learning Using Weak Supervision (2020)0.00
• Learning To Advise And Learning From Advice In Cooperative Multi-agent Reinforcement Learning (2022)0.00