TextWorld: A Learning Environment for Text-based Games (1806.11532v2)

Abstract: We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.

• The paper introduces a flexible reinforcement learning sandbox that uses logical state representations to generate diverse text-based games.
• The paper employs forward and backward quest generation strategies to create both handcrafted and automatically generated game scenarios.
• The paper evaluates baseline RL agents on benchmark tasks, highlighting challenges in exploration and sparse rewards in rich text environments.

Overview of TextWorld: A Learning Environment for Text-based Games

This paper introduces TextWorld, a sophisticated sandbox learning environment designed for training and evaluating reinforcement learning (RL) agents specifically on text-based games. TextWorld is implemented as a Python library and offers a comprehensive suite of functionalities, including interactive game play, state tracking, and reward assignment. It also empowers users to either handcraft or automatically generate new games through its generative mechanisms. This paper provides a formal framework for understanding text-based games within the context of RL, highlighting the unique challenges they present, such as partial observability and large, sparse action spaces.

Key Features

The main contribution of TextWorld is its ability to generate a wide range of text-based games of varying complexity from a set of underlying world mechanics. This is reminiscent of other sandbox environments like SimCity or MazeBase, but with a focus on language and the associated challenges inherent in text-based games. Some notable features of TextWorld include:

• State Representation and Transition Function: Utilization of logical atoms and linear logic to represent game states and transitions, respectively. This allows for a non-trivial definition of the state space and provides a robust method for generating game worlds.
• Generative Capabilities: TextWorld employs both forward and backward quest generation strategies to create diverse games. This flexibility enables the generation of structured and meaningful quests, enhancing the environment's utility for RL research.
• Text Generation and Observation: Leveraging context-free grammars, TextWorld can generate descriptions, instructions, and object names that enrich the narrative quality of games while providing varied observational text to RL agents.

Strengths and Contributions

TextWorld's primary strengths lie in its capacity to control the difficulty, scope, and language of games through precise generative algorithms. This allows researchers to design experiments that focus on particular RL challenges like exploration, credit assignment with sparse rewards, and generalization. The environment also facilitates curriculum learning and transfer learning by generating sets of varied yet similar games, enabling researchers to systematically paper these phenomena in RL.

The paper outlines several benchmark tasks and evaluates baseline algorithms on these as well as on a curated list of hand-authored games. The results from these evaluations underscore the difficulties current RL methods face in mastering even moderately complex text-based tasks, while also demonstrating the potential of TextWorld to act as a "training ground" for novel RL approaches.

Implications and Future Prospects

The introduction of TextWorld marks an important step forward in the field of interactive fiction and text-based gaming as it relates to machine learning and artificial intelligence. By providing a controlled environment to systematically explore the peculiar challenges of text-based games, it lays the groundwork for advancements in natural language understanding, planning, goal-oriented dialogue, and dynamic task execution within RL contexts.

Looking ahead, future developments could include expanding TextWorld’s library of logical rules and actions to encompass more complex game mechanics, integrating multi-agent scenarios, and incorporating more sophisticated NLP techniques for both the interpretation and generation of game text. Furthermore, the insights gained from agent behavior in TextWorld can potentially translate to applications in real-world scenarios that similarly require parsing and generating natural language in interactive settings.

In conclusion, TextWorld offers a rich, flexible, and instructive platform for pushing the boundaries of RL in text-centric environments. It addresses critical gaps in current research tools and presents opportunities for future exploration in AI, especially in contexts where language and sequential decision-making intersect.