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Title: Graph Constrained Reinforcement Learning for Natural Language Action Spaces

Paper content:
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.
Natural language communication has long been considered a defining characteristic of human intelligence.
We are motivated by the question of how learning agents can understand and generate contextually relevant natural language in service of achieving a goal.
In pursuit of this objective we study Interactive Fiction (IF) games, or text-adventures: simulations in which an agent interacts with the world purely through natural language-"seeing" and "talking" to the world using textual descriptions and commands.
To progress in these games, an agent must generate natural language actions that are coherent, contextually relevant, and able to effect the desired change in the world.
Complicating the problem of generating contextually relevant language in these games is the issue of partial observability: the fact that the agent never has access to the true underlying world state.
IF games are structured as puzzles and often consist of an complex, interconnected web of distinct locations, objects, and characters.
The agent needs to thus reason about the complexities of such a world solely through the textual descriptions that it receives, descriptions that are often incomplete.
Further, an agent must be able to perform commonsense reasoning-IF games assume that human players possess prior commonsense and thematic knowledge (e.g. knowing that swords can kill trolls or that trolls live in dark places).
Knowledge graphs provide us with an intuitive way of representing these partially observable worlds.
Prior works have shown how using knowledge graphs aids in the twin issues of partial observability (Ammanabrolu & Riedl, 2019a) and commonsense reasoning (Ammanabrolu & Riedl, 2019b ), but do not use them in the context of generating natural language.
To gain a sense for the challenges surrounding natural language generation, we need to first understand how large this space really is.
In order to solve solve a popular IF game such as Zork1 it's necessary to generate actions consisting of up to five-words from a relatively modest vocabulary of 697 words recognized by Zork's parser.
Even this modestly sized vocabulary leads to O(697 5 ) = 1.64 × 10 the structure required to further constrain our action space via our knowledge graph-and make the argument that the combination of these approaches allows us to generate meaningful natural language commands.
Our contributions are as follows: We introduce an novel agent that utilizes both a knowledge graph based state space and template based action space and show how to train such an agent.
We then conduct an empirical study evaluating our agent across a diverse set of IF games followed by an ablation analysis studying the effectiveness of various components of our algorithm as well as its overall generalizability.
Remarkably we show that our agent achieves state-of-the-art performance on a large proportion of the games despite the exponential increase in action space size.
Tabula rasa reinforcement learning offers an intuitive paradigm for exploring goal driven, contextually aware natural language generation.
The sheer size of the natural language action space, however, has proven to be out of the reach of existing algorithms.
In this paper we introduced KG-A2C, a novel learning agent that demonstrates the feasibility of scaling reinforcement learning towards natural language actions spaces with hundreds of millions of actions.
The key insight to being able to efficiently explore such large spaces is the combination of a knowledge-graph-based state space and a template-based action space.
The knowledge graph serves as a means for the agent to understand its surroundings, accumulate information about the game, and disambiguate similar textual observations while the templates lend a measure of structure that enables us to exploit that same knowledge graph for language generation.
Together they constrain the vast space of possible actions into the compact space of sensible ones.
An ablation study on Zork1 shows state-of-the-art performance with respect to any currently existing general reinforcement learning agent, including those with action spaces six orders of magnitude smaller than what we consider-indicating the overall efficacy of our combined state-action space.
Further, a suite of experiments shows wide improvement over TDQN, the current state-of-the-art template based agent, across a diverse set of 26 human-made IF games covering multiple genres and game structures demonstrate that our agent is able to generalize effectively.
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assistant: text: We present KG-A2C, a reinforcement learning agent that builds a dynamic knowledge graph while exploring and generates natural language using a template-based action space - outperforming all current agents on a wide set of text-based games.