Conditional Imitation Learning for Multi-Agent Games

• URL: http://arxiv.org/abs/2201.01448v1
• Date: Wed, 5 Jan 2022 04:40:13 GMT
• Title: Conditional Imitation Learning for Multi-Agent Games
• Authors: Andy Shih and Stefano Ermon and Dorsa Sadigh
• Abstract summary: We study the problem of conditional multi-agent imitation learning, where we have access to joint trajectory demonstrations at training time. We propose a novel approach to address the difficulties of scalability and data scarcity. Our model learns a low-rank subspace over ego and partner agent strategies, then infers and adapts to a new partner strategy by interpolating in the subspace.
• Score: 89.897635970366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While advances in multi-agent learning have enabled the training of increasingly complex agents, most existing techniques produce a final policy that is not designed to adapt to a new partner's strategy. However, we would like our AI agents to adjust their strategy based on the strategies of those around them. In this work, we study the problem of conditional multi-agent imitation learning, where we have access to joint trajectory demonstrations at training time, and we must interact with and adapt to new partners at test time. This setting is challenging because we must infer a new partner's strategy and adapt our policy to that strategy, all without knowledge of the environment reward or dynamics. We formalize this problem of conditional multi-agent imitation learning, and propose a novel approach to address the difficulties of scalability and data scarcity. Our key insight is that variations across partners in multi-agent games are often highly structured, and can be represented via a low-rank subspace. Leveraging tools from tensor decomposition, our model learns a low-rank subspace over ego and partner agent strategies, then infers and adapts to a new partner strategy by interpolating in the subspace. We experiments with a mix of collaborative tasks, including bandits, particle, and Hanabi environments. Additionally, we test our conditional policies against real human partners in a user study on the Overcooked game. Our model adapts better to new partners compared to baselines, and robustly handles diverse settings ranging from discrete/continuous actions and static/online evaluation with AI/human partners.

Related papers

• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• Mimicking To Dominate: Imitation Learning Strategies for Success in Multiagent Competitive Games [13.060023718506917]
  We develop a new multi-agent imitation learning model for predicting next moves of the opponents. We also present a new multi-agent reinforcement learning algorithm that combines our imitation learning model and policy training into one single training process. Experimental results show that our approach achieves superior performance compared to existing state-of-the-art multi-agent RL algorithms.
  arXiv  Detail & Related papers  (2023-08-20T07:30:13Z)
• A Hierarchical Approach to Population Training for Human-AI Collaboration [20.860808795671343]
  We introduce a Hierarchical Reinforcement Learning (HRL) based method for Human-AI Collaboration. We demonstrate that our method is able to dynamically adapt to novel partners of different play styles and skill levels in the 2-player collaborative Overcooked game environment.
  arXiv  Detail & Related papers  (2023-05-26T07:53:12Z)
• Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents [120.91291581594773]
  We present a formal formulation of a cooperative multi-agent reinforcement learning system with unexpected crashes. We propose a coach-assisted multi-agent reinforcement learning framework, which introduces a virtual coach agent to adjust the crash rate during training. To the best of our knowledge, this work is the first to study the unexpected crashes in the multi-agent system.
  arXiv  Detail & Related papers  (2022-03-16T08:22:45Z)
• On the Critical Role of Conventions in Adaptive Human-AI Collaboration [73.21967490610142]
  We propose a learning framework that teases apart rule-dependent representation from convention-dependent representation. We experimentally validate our approach on three collaborative tasks varying in complexity.
  arXiv  Detail & Related papers  (2021-04-07T02:46:19Z)
• Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams [0.0]
  We develop a competitive multi agent environment called FortAttack in which two teams compete against each other. We observe a natural emergence of heterogeneous behavior amongst homogeneous agents when such behavior can lead to the team's success. We propose ensemble training, in which we utilize the evolved opponent strategies to train a single policy for friendly agents.
  arXiv  Detail & Related papers  (2020-07-06T22:35:56Z)
• Learning to Model Opponent Learning [11.61673411387596]
  Multi-Agent Reinforcement Learning (MARL) considers settings in which a set of coexisting agents interact with one another and their environment. This poses a great challenge for value function-based algorithms whose convergence usually relies on the assumption of a stationary environment. We develop a novel approach to modelling an opponent's learning dynamics which we term Learning to Model Opponent Learning (LeMOL)
  arXiv  Detail & Related papers  (2020-06-06T17:19:04Z)
• Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game [71.24825724518847]
  We present a study on how popular reinforcement learning algorithms can be adapted to learn and to play a real-world implementation of a competitive multiplayer card game. We propose specific training and validation routines for the learning agents, in order to evaluate how the agents learn to be competitive and explain how they adapt to each others' playing style.
  arXiv  Detail & Related papers  (2020-04-08T14:11:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.