Exploring Text-Based Worlds: A Deep Reinforcement Learning Approach

In this paper, the authors propose a new platform called Textworld, designed to train and evaluate agents in text-based games. The platform is based on the idea that language models can be used to generate and manipulate game content, allowing agents to learn how to play games through trial and error.
The authors explain that traditional approaches to training AI agents in games have relied on hand-crafted features or simple reinforcement learning methods, which can be limited in their ability to capture complex game dynamics. In contrast, Textworld uses a combination of natural language processing (NLP) techniques and deep reinforcement learning to train agents in a variety of text-based games.
The platform consists of three main components: a text encoder, a value network, and an actor network. The text encoder generates game content based on a given prompt or input, while the value network estimates the expected return for each action taken by the agent. The actor network then selects actions based on this estimated return.
The authors demonstrate the effectiveness of Textworld through experiments on several different games, including tic-tac-toe and snake. They show that their approach can learn to play these games at a high level of performance without requiring any prior knowledge or domain-specific tuning.
One key insight from the paper is that the quality of the generated game content can have a significant impact on the performance of the agents. The authors find that using higher-quality text inputs can lead to better learning outcomes and more robust agent behavior.
Overall, Textworld represents an exciting new direction in the field of AI research, demonstrating the potential for language models to enable more sophisticated and flexible training of agents in a variety of domains. By leveraging the expressive power of natural language, the platform opens up new possibilities for creating and evaluating intelligent systems that can interact with complex game environments.