687 citations · 1.4k across the 25 of their papers we have counts for
6 papers · 1 filter
MemWM: Memory-Augmented Text-Based World Model
Yujun Wang, Tao Zhang, Jinhe Bi +9
World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent next-state predictions can sti…
How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds
Prithviraj Ammanabrolu, Ethan Tien, Matthew Hausknecht +1
Text-based games are long puzzles or quests, characterized by a sequence of sparse and potentially deceptive rewards. They provide an ideal platform to develop agents that perceive…
Interactive Fiction Games: A Colossal Adventure
Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Côté +1
A hallmark of human intelligence is the ability to understand and communicate with language. Interactive Fiction games are fully text-based simulation environments where a player i…
NAIL: A General Interactive Fiction Agent
Matthew Hausknecht, Ricky Loynd, Greg Yang +2
Interactive Fiction (IF) games are complex textual decision making problems. This paper introduces NAIL, an autonomous agent for general parser-based IF games. NAIL won the 2018 Te…
Neural Program Meta-Induction
Jacob Devlin, Rudy Bunel, Rishabh Singh +2
Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output…
Deep Reinforcement Learning in Parameterized Action Space
Matthew Hausknecht, Peter Stone
Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and acti…