46 research outputs found
Learning to win by reading manuals in a Monte-Carlo framework
This paper presents a novel approach for leveraging automatically extracted textual knowledge to improve the performance of control applications such as games. Our ultimate goal is to enrich a stochastic player with high-level guidance expressed in text. Our model jointly learns to identify text that is relevant to a given game state in addition to learning game strategies guided by the selected text. Our method operates in the Monte-Carlo search framework, and learns both text analysis and game strategies based only on environment feedback. We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent significantly outperforms its language-unaware counterpart, yielding a 27% absolute improvement and winning over 78% of games when playing against the built-in AI of Civilization II.National Science Foundation (U.S.) (CAREER grant IIS-0448168)National Science Foundation (U.S.) (CAREER grant IIS-0835652)United States. Defense Advanced Research Projects Agency (DARPA Machine Reading Program (FA8750-09- C-0172))Microsoft Research (New Faculty Fellowship
Language Understanding for Text-based Games Using Deep Reinforcement Learning
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.Comment: 11 pages, Appearing at EMNLP, 201
Deep Reinforcement Learning for Dialogue Generation
Recent neural models of dialogue generation offer great promise for
generating responses for conversational agents, but tend to be shortsighted,
predicting utterances one at a time while ignoring their influence on future
outcomes. Modeling the future direction of a dialogue is crucial to generating
coherent, interesting dialogues, a need which led traditional NLP models of
dialogue to draw on reinforcement learning. In this paper, we show how to
integrate these goals, applying deep reinforcement learning to model future
reward in chatbot dialogue. The model simulates dialogues between two virtual
agents, using policy gradient methods to reward sequences that display three
useful conversational properties: informativity (non-repetitive turns),
coherence, and ease of answering (related to forward-looking function). We
evaluate our model on diversity, length as well as with human judges, showing
that the proposed algorithm generates more interactive responses and manages to
foster a more sustained conversation in dialogue simulation. This work marks a
first step towards learning a neural conversational model based on the
long-term success of dialogues
Text-based Adventures of the Golovin AI Agent
The domain of text-based adventure games has been recently established as a
new challenge of creating the agent that is both able to understand natural
language, and acts intelligently in text-described environments.
In this paper, we present our approach to tackle the problem. Our agent,
named Golovin, takes advantage of the limited game domain. We use genre-related
corpora (including fantasy books and decompiled games) to create language
models suitable to this domain. Moreover, we embed mechanisms that allow us to
specify, and separately handle, important tasks as fighting opponents, managing
inventory, and navigating on the game map.
We validated usefulness of these mechanisms, measuring agent's performance on
the set of 50 interactive fiction games. Finally, we show that our agent plays
on a level comparable to the winner of the last year Text-Based Adventure AI
Competition