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Learning to Cooperate with Humans using Generative Agents (2411.13934v1)

Published 21 Nov 2024 in cs.LG, cs.AI, and cs.MA

Abstract: Training agents that can coordinate zero-shot with humans is a key mission in multi-agent reinforcement learning (MARL). Current algorithms focus on training simulated human partner policies which are then used to train a Cooperator agent. The simulated human is produced either through behavior cloning over a dataset of human cooperation behavior, or by using MARL to create a population of simulated agents. However, these approaches often struggle to produce a Cooperator that can coordinate well with real humans, since the simulated humans fail to cover the diverse strategies and styles employed by people in the real world. We show \emph{learning a generative model of human partners} can effectively address this issue. Our model learns a latent variable representation of the human that can be regarded as encoding the human's unique strategy, intention, experience, or style. This generative model can be flexibly trained from any (human or neural policy) agent interaction data. By sampling from the latent space, we can use the generative model to produce different partners to train Cooperator agents. We evaluate our method -- \textbf{G}enerative \textbf{A}gent \textbf{M}odeling for \textbf{M}ulti-agent \textbf{A}daptation (GAMMA) -- on Overcooked, a challenging cooperative cooking game that has become a standard benchmark for zero-shot coordination. We conduct an evaluation with real human teammates, and the results show that GAMMA consistently improves performance, whether the generative model is trained on simulated populations or human datasets. Further, we propose a method for posterior sampling from the generative model that is biased towards the human data, enabling us to efficiently improve performance with only a small amount of expensive human interaction data.

Summary

  • The paper introduces GAMMA, a novel generative model using a VAE to simulate diverse human strategies for enhanced cooperation.
  • It demonstrates improved AI-human team performance in Overcooked simulations by adapting to strategies sampled from limited human data.
  • The approach marks a paradigm shift from static behavior cloning to dynamic multi-agent adaptation, paving the way for advanced human-AI collaborations.

Overview of "Learning to Cooperate with Humans using Generative Agents"

The paper under discussion, titled "Learning to Cooperate with Humans using Generative Agents," addresses a pivotal challenge in multi-agent reinforcement learning (MARL): training AI agents to effectively coordinate with zero-shot human partners. Existing approaches in this domain typically leverage behavior cloning or MARL-based simulated human policies to train cooperative AI agents. However, these methods often fall short when it comes to interacting with real humans due to the limited representation of the myriad strategies humans employ. This research introduces a novel method named Generative Agent Modeling for Multi-agent Adaptation (GAMMA), emphasizing a generative model capable of simulating a broader spectrum of human behaviors.

Key Contributions

  • Generative Modeling of Human Strategies: The authors propose using generative modeling to encapsulate the diverse strategic approaches humans might take, allowing the AI to envision more realistic and varied scenarios than discrete, predefined agent behaviors. This approach utilizes a variational autoencoder (VAE) to learn a latent variable representation indicative of human strategies, intentions, experience, or style from interaction data.
  • Training Adaptive Cooperators: GAMMA generates diverse partner strategies by sampling from the latent space, training Cooperator agents to adapt to a broad range of potential human behaviors. The research highlights the effectiveness of GAMMA across scenarios with agents trained on both simulated and real human datasets.
  • Human-Adaptive Sampling: A significant enhancement is proposed for efficiently using a limited amount of human interaction data to bias the posterior sampling from the generative model towards more human-like strategies. This adjustment ensures better alignment of AI responses with real human strategies, optimizing the AI's performance with minimal human data input.

Evaluation

The efficacy of GAMMA is evaluated using the Overcooked simulation, a cooperative tasks game requiring seamless coordination. By deploying a user paper involving real human participants, the paper substantiates notable improvements in AI-human team performance, demonstrating the robustness of agents trained with the GAMMA model over those from other competitive MARL approaches.

Results and Implications

The results suggest that GAMMA significantly enhances the performance of cooperative AI agents when partnered with human counterparts. This method provides consistent improvements using both synthetic simulated agent populations and limited human data. The user paper reinforces the claim that generative models can efficiently traverse and cover the strategy space, leading to superior coordination outcomes with humans compared to traditional MARL frameworks.

The implications of this work are profound, advocating for a paradigm shift in MARL from static behavior cloning and rigid MARL models to dynamic, generative approaches that can better encapsulate the variability in human strategies. Such an advancement could potentially transform various domains requiring human-AI collaboration, such as robotics, digital assistants, and cooperative gameplay, positioning generative models as indispensable tools in developing adaptive AI partners capable of understanding and anticipating human actions in real-time.

Future Directions

This paper lays a foundation but also opens avenues for further exploration. Future research could expand the generative models' capabilities to multi-agent systems beyond the two-player setup, optimize the use of generative models in real-time applications, and integrate additional real-world uncertainties and constraints into the generative strategy space. Further studies could also explore the interplay between generative models and other emerging domains in AI to enhance human-AI interaction models further.

Overall, "Learning to Cooperate with Humans using Generative Agents" provides a compelling case for the use of generative models in enhancing AI's adaptability and cooperative abilities with humans, marking a pivotal development in how AI systems can be trained to understand and work alongside human counterparts.

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