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A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worlds

Published 9 May 2024 in cs.CL and cs.AI | (2405.06059v1)

Abstract: Open-ended worlds are those in which there are no pre-specified goals or environmental reward signal. As a consequence, an agent must know how to perform a multitude of tasks. However, when a new task is presented to an agent, we expect it to be able to reuse some of what it knows from previous tasks to rapidly learn that new task. We introduce a novel technique whereby policies for different a priori known tasks are combined into a Mixture-of-Experts model with an attention mechanism across a mix of frozen and unfrozen experts. The model learns when to attend to frozen task-specific experts when appropriate and learns new experts to handle novel situations. We work in an open-ended text-based environment in which the agent is tasked with behaving like different types of character roles and must rapidly learn behaviors associated with new character role types. We show that our agent both obtains more rewards in the zero-shot setting, and discovers these rewards with greater sample efficiency in the few-shot learning settings.

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Summary

  • The paper demonstrates that combining frozen and trainable experts via an attention mechanism improves few-shot task transfer in text-based games.
  • The approach leverages pre-trained role-specific policies to handle new tasks, achieving superior zero-shot performance and heightened sample efficiency over baselines.
  • The method’s success in role-playing scenarios suggests broader applications for dynamic decision-making in systems such as automated customer service.

Understanding Mixture-of-Experts in Open-Ended Text-Based Environments

Introduction to Mixture-of-Experts

The paper introduces an innovative approach using a Mixture-of-Experts (MoE) model, enhanced by an attention mechanism, to handle task transfer in open-ended text-based environments. This model showcases its ability to combine existing, pre-trained experts for known tasks while incorporating a new "expert" when faced with unfamiliar tasks. The setup is carefully crafted around text-based role-playing game scenarios, such as ones often found in Dungeons & Dragons style games.

How the MoE Approach Works

Combining Expertise

The core concept relies on combining multiple expert policies, where each policy is adept at a specific character role or task. These policies are referred to as "experts," and each one is frozen — meaning their parameters do not change during the learning of a new task. An additional expert, which is not pre-trained, remains active and trainable. This structure allows the system to reference the frozen experts for tasks they know well, while the trainable expert adapts to new requirements.

Attention Mechanism

Each expert proposes an action based on the current game scenario described in natural language. The attention mechanism then dynamically decides which expert's suggestion to prioritize based on the context of the situation. This decision is influenced by the expected rewards and efficiencies mapped out by each expert's policy, directing the model's focus seamlessly among the varying expert opinions.

Experiments and Results

Setup

To validate their model, the authors create a simulated environment reflecting a typical open-ended role-playing game with multiple character roles and interactions. Several pre-trained experts corresponding to specific roles (like adventurers, thieves, etc.) are tasked with new roles through the model's architecture.

Effectiveness

The results show that the MoE model not only performs better in a zero-shot setting (where the task is entirely new) but also exhibits greater sample efficiency during the few-shot learning scenarios. This efficiency indicates that the model learns to perform new tasks faster than the baselines that either start from scratch or fine-tune from a pre-trained state.

Theoretical and Practical Implications

Broader Applications

While the study's focus is on text-based games, the implications extend into any domain requiring dynamic decision-making across a spectrum of expertise. This could include areas like automated customer service, where different types of inquiries might be better handled by specialized systems trained on specific types of requests.

Future AI Developments

The integration of a trainable expert alongside multiple frozen ones provides a pathway toward more adaptive AI systems that can leverage existing knowledge while continuously learning new information. This method could lead to AI that is both more efficient (due to expert reuse) and more capable in novel scenarios.

Concluding Thoughts

The Mixture-of-Experts model with an incorporated attention mechanism offers an intriguing solution to the issue of task transfer in complex, multifaceted environments where multiple tasks or roles might be encountered. By harnessing and combining the strengths of different expert policies, and simultaneously adapting to new situations, AI systems can potentially handle a broader range of tasks with higher efficiency and effectiveness. Future research might explore the scalability of this approach and its application across diverse domains beyond gaming.

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