Graph Constrained Reinforcement Learning for Natural Language Action Spaces (2001.08837v1)

Abstract: Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in combinatorially-large text-based action spaces. We present KG-A2C, an agent that builds a dynamic knowledge graph while exploring and generates actions using a template-based action space. We contend that the dual uses of the knowledge graph to reason about game state and to constrain natural language generation are the keys to scalable exploration of combinatorially large natural language actions. Results across a wide variety of IF games show that KG-A2C outperforms current IF agents despite the exponential increase in action space size.

Graph Constrained Reinforcement Learning for Natural Language Action Spaces

The paper "Graph Constrained Reinforcement Learning for Natural Language Action Spaces" presents a novel approach for addressing the challenges inherent in natural language understanding and action generation within text-based environments, specifically Interactive Fiction (IF) games. The research introduces a reinforcement learning (RL) agent, KG-A2C, which leverages a dynamically constructed knowledge graph to enhance its decision-making processes in vast natural language action spaces.

Interactive Fiction games, characterized by text-based interaction where agents must interpret and act upon textual descriptions, pose unique problems for RL agents due to the sheer size of the possible action space and partial observability of the environment. The action space in IF games can be astronomical, as illustrated by the complexities in generating actions from a vocabulary as seen in popular games like Zork1, where the possible actions can reach the magnitude of $10^{14}$. Due to this, the agent must not only understand language but also generate coherent and rewarding sequences of actions from this combinatorial space.

The core innovation of the KG-A2C agent lies in its utilization of a knowledge graph as a state representation. By continuously updating this graph throughout its exploration of the game, KG-A2C can track relationships between objects, characters, and locations, introducing a layer of commonsense reasoning into the decision-making process. The knowledge graph facilitates understanding of partially observable states, thus allowing the agent to infer latent structures within the game's narrative.

In tandem with the knowledge graph, the paper adopts a template-based action space for the agent's action generation. This template-based approach reduces the action space's vastness by structuring potential actions into pre-defined templates, which are dynamically filled with appropriate vocabulary based on the state as represented by the knowledge graph. This dual-constrained approach effectively manages the action space size, allowing the agent to explore more meaningfully with reduced computational overhead.

The paper details significant empirical evaluations of KG-A2C, demonstrating superior performance over existing text-based agents across various IF games. The agent achieves state-of-the-art performance in a majority of the games tested, indicating the efficacy of integrating a knowledge graph with a strategically constrained action template. Results highlight that the combination of graph-based state representation and template-based action ensures efficient exploration and effective policy learning even in environments with exponential action space sizes.

In addition to its empirical contributions, the research provides a thorough exploration of knowledge graphs' role in natural language processing tasks within the confines of gaming. It opens new avenues for the application of RL in other complex, text-based interaction spaces such as chatbots and automated narrative generation systems. The broader implications suggest potential for bridging the gap between RL and language-based AI systems, advancing towards more interactive, narrative-driven AI companions.

The paper sheds light on the intersection of natural language processing, graph theory, and reinforcement learning, suggesting future research directions such as enhancing the generalizability of knowledge graph construction or exploring other RL architectures that can benefit from constraint-based reasoning. The approach serves as a cornerstone for AI systems that seek to understand and generate natural language in dynamically evolving, text-intensive environments.