Language Understanding for Text-based Games Using Deep Reinforcement Learning (1506.08941v2)

Abstract: In this paper, we consider the task of learning control policies for text-based games. In these games, all interactions in the virtual world are through text and the underlying state is not observed. The resulting language barrier makes such environments challenging for automatic game players. We employ a deep reinforcement learning framework to jointly learn state representations and action policies using game rewards as feedback. This framework enables us to map text descriptions into vector representations that capture the semantics of the game states. We evaluate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state representations. Our algorithm outperforms the baselines on both worlds demonstrating the importance of learning expressive representations.

Deep Reinforcement Learning for Language Understanding in Text-Based Games

The paper "Language Understanding for Text-based Games using Deep Reinforcement Learning" by Narasimhan, Kulkarni, and Barzilay presents a novel framework for training AI systems to effectively navigate and solve challenges in text-based game environments. These environments, characterized by their reliance on textual interactions, pose significant challenges due to their indirect representation of states and the complexity of natural language.

Core Contributions

The primary contribution of this research is a deep reinforcement learning (RL) approach that jointly learns state representations and action policies from textual inputs and feedback from the game environment. The proposed framework employs Long Short-Term Memory (LSTM) networks to process sequences of words and develop semantic representations of game states. These representations are then utilized within a Deep Q-Network (DQN) to learn optimal policies for interacting with the game.

The paper is novel in its integration of natural language processing with reinforcement learning to tackle the issues of uncertainty and variability inherent in text-based games. By transforming textual descriptions into vector representations, the methodology effectively bridges the language-action gap, enabling agents to better comprehend and interact with their environments.

Evaluation and Results

The authors evaluate their model on two distinct Multi-User Dungeon (MUD) games: a simpler Home world and a complex Fantasy world, the latter featuring extensive human-generated textual content. Comparisons between the LSTM-DQN model and baseline models that rely on bag-of-words (BOW) or bag-of-bigrams (BI) representations highlight the superior performance of the LSTM-DQN. For instance, in the Fantasy world, the LSTM-DQN completes 96% of quests, substantially outperforming the BOW-DQN and the random baseline.

Notably, the paper illustrates the importance of learned representations through transfer learning experiments, where representations learned in one environment significantly accelerate learning in a restructured variant of that environment. Furthermore, prioritized experience replay is shown to enhance the learning efficiency of the LSTM-DQN, affirming the utility of focusing on informative experiences during training.

Implications and Future Directions

The implications of this work are multifaceted. Practically, it demonstrates the potential for AI agents to engage with and complete complex tasks in environments that require sophisticated language understanding. Theoretically, it highlights the capability of neural networks to derive compact, rich representations from natural language, which can be leveraged for effective decision-making.

For future developments, extending the framework to incorporate more advanced planning and strategy learning capabilities could enhance performance on tasks requiring long-term reasoning. Additionally, exploring the integration of external knowledge sources could further refine the agent's ability to understand and act upon complex narrative contexts.

Overall, this work stands as a significant milestone in the intersection of reinforcement learning and natural language processing, showcasing a viable path towards creating intelligent agents capable of comprehending and navigating complex, text-rich environments.