Situated Dialogue Learning through Procedural Environment Generation (2110.03262v2)

Abstract: We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums. Our agents operate in LIGHT (Urbanek et al. 2019) -- a large-scale crowd-sourced fantasy text adventure game wherein an agent perceives and interacts with the world through textual natural language. Goals in this environment take the form of character-based quests, consisting of personas and motivations. We augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents to achieve such goals. In particular, we measure curriculum difficulty in terms of the rarity of the quest in the original training distribution -- an easier environment is one that is more likely to have been found in the unaugmented dataset. An ablation study shows that this method of learning from the tail of a distribution results in significantly higher generalization abilities as measured by zero-shot performance on never-before-seen quests.

• The paper demonstrates that procedurally generated environments enable goal-driven agents to tackle increasingly complex, interactive quests effectively.
• The proposed curriculum framework scales quest difficulty by quest rarity, significantly boosting agents' zero-shot generalization in novel scenarios.
• Leveraging retrieval models and generative techniques, the study paves the way for more adaptable and realistic dialogue-based AI training.

Overview of "Situated Dialogue Learning through Procedural Environment Generation"

The paper authored by Ammanabrolu et al. introduces an innovative approach to developing goal-driven agents capable of interacting in text-based environments through both actions and dialogues. The focus is set in the LIGHT environment—a large-scale, crowdsourced interactive fantasy text game—in which agents immerse themselves in character-driven quests that require nuanced understanding and engagement within richly described textual worlds.

The core contribution of this work lies in the procedural generation of novel environments and quests, structured to progressively increase in complexity. This addresses a significant challenge in reinforcement learning (RL): generalizing agents' performance beyond memorized solutions to unfamiliar scenarios. The authors propose a curriculum learning framework where the environment's difficulty is gauged by the rarity of quest types in the training data, thus allowing more balanced exposure across varying tasks. Such procedural generation methods inherently broaden the potential state-action space, fostering improved generalization capabilities of the trained agents. This was evidenced by the superior zero-shot performance on quests that were not part of the training distribution.

Procedural Environment Generation

The procedure commences with generating an initial character and retrieving a suitable starting location using pre-trained StarSpace and biencoder retrieval models, respectively. Subsequently, the quest is crafted utilizing generative models like BART, particularly focusing on understanding character motivations and associated goal states. The complexity ramps up with the introduction of additional characters, locations, and objects through retrieval models to ensure the achievement of quest objectives, culminating in a dynamically generated game world.

Curriculum Learning and Evaluation

The paper comprehensively analyzes the curriculum learning aspect by varying the span between the most and least common quest types, establishing this distribution as a critical factor in agent training efficacy. The empirical results are unambiguous: agents trained via the proposed procedural curriculum systematically outperform those restricted to single-task environments, with curriculum training reporting markedly enhanced goal-attainment rates both in acting and dialogue tasks. This holds true under both scratch and pre-trained settings.

Implications and Future Research Directions

From a practical standpoint, this paper provides a framework for constructing adaptable agents capable of learning complex interactions in dynamic settings. The employed methodologies herald a potential shift in how agents can be prepared for task variability and unforeseen environmental contexts often encountered in real-world applications, such as interactive virtual assistants and educational software.

The theoretical implications underscore the potency of procedurally generated environments in refining autonomous learning processes. As the field progresses, infusing more realistic constraints and expanding the diversity of generated scenarios could further elevate agent preparedness. Future work may explore the integration of advanced dialogue models and explore the utilization of this framework in multi-agent cooperative settings to better mirror real-life complexity.

In summary, this paper contributes substantially to the agent-based learning literature by demonstrating a pathway to train interactively competent agents through a novel, synthetically generated and progressively challenging curriculum framework. Such research forms the backbone of advancing AI systems’ capabilities to manage open-ended, interactive tasks with increased dexterity and relevance.