687 citations · 1.4k across the 25 of their papers we have counts for
26 papers
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…
UniMASK: Unified Inference in Sequential Decision Problems
Micah Carroll, Orr Paradise, Jessy Lin +8
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same…
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control
Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri +3
Simulated humanoids are an appealing research domain due to their physical capabilities. Nonetheless, they are also challenging to control, as a policy must drive an unstable, disc…
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers
Micah Carroll, Jessy Lin, Orr Paradise +8
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same…
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté +5
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedur…
Consistent Dropout for Policy Gradient Reinforcement Learning
Matthew Hausknecht, Nolan Wagener
Dropout has long been a staple of supervised learning, but is rarely used in reinforcement learning. We analyze why naive application of dropout is problematic for policy-gradient…
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents
Shunyu Yao, Karthik Narasimhan, Matthew Hausknecht
Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the mot…
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon +20
The NeurIPS 2020 Procgen Competition was designed as a centralized benchmark with clearly defined tasks for measuring Sample Efficiency and Generalization in Reinforcement Learning…
Keep CALM and Explore: Language Models for Action Generation in Text-based Games
Shunyu Yao, Rohan Rao, Matthew Hausknecht +1
Text-based games present a unique challenge for autonomous agents to operate in natural language and handle enormous action spaces. In this paper, we propose the Contextual Action…
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté +3
Given a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of succe…
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…
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
Prithviraj Ammanabrolu, Matthew Hausknecht
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 ext…
Working Memory Graphs
Ricky Loynd, Roland Fernandez, Asli Celikyilmaz +2
Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text sequences. Inspired by this trend, we study the…
Learning Calibratable Policies using Programmatic Style-Consistency
Eric Zhan, Albert Tseng, Yisong Yue +2
We study the problem of controllable generation of long-term sequential behaviors, where the goal is to calibrate to multiple behavior styles simultaneously. In contrast to the wel…
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…
Multi-Preference Actor Critic
Ishan Durugkar, Matthew Hausknecht, Adith Swaminathan +1
Policy gradient algorithms typically combine discounted future rewards with an estimated value function, to compute the direction and magnitude of parameter updates. However, for m…
ScriptNet: Neural Static Analysis for Malicious JavaScript Detection
Jack W. Stokes, Rakshit Agrawal, Geoff McDonald +1
Malicious scripts are an important computer infection threat vector in the wild. For web-scale processing, static analysis offers substantial computing efficiencies. We propose the…
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…
TextWorld: A Learning Environment for Text-based Games
Marc-Alexandre Côté, Ákos Kádár, Xingdi Yuan +10
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play…
Counting to Explore and Generalize in Text-based Games
Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni +4
We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments. We show promising results on a set of g…
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin +2
Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program sy…
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…
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
Marlos C. Machado, Marc G. Bellemare, Erik Talvitie +3
The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It support…
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…