17 citations · 43 across the 11 of their papers we have counts for
13 papers
Waypoint-1.5: A Real-Time Video World Model for Consumer Hardware
Rajit Rajpal, Shahbuland Matiana, Liew Wei Pyn +13
We present Waypoint 1.5, a real-time diffusion world model for interactive video generation on consumer-grade hardware. Unlike general video diffusion models, interactive world mod…
COMBAT: Conditional World Models for Behavioral Agent Training
Anmol Agarwal, Pranay Meshram, Sumer Singh +5
Recent advances in video generation have spurred the development of world models capable of simulating 3D-consistent environments and interactions with static objects. However, a s…
The Goofus & Gallant Story Corpus for Practical Value Alignment
Md Sultan Al Nahian, Tasmia Tasrin, Spencer Frazier +2
Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order.…
Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories
Md Sultan Al Nahian, Spencer Frazier, Brent Harrison +1
Value alignment is the task of creating autonomous systems whose values align with those of humans. Past work has shown that stories are a potentially rich source of information on…
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning
Louis Castricato, Alexander Havrilla, Shahbuland Matiana +5
Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. Existing methods to control…
Automated Story Generation as Question-Answering
Louis Castricato, Spencer Frazier, Jonathan Balloch +2
Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work towar…
Cut the CARP: Fishing for zero-shot story evaluation
Shahbuland Matiana, JR Smith, Ryan Teehan +4
Recent advances in large-scale language models (Raffel et al., 2019; Brown et al., 2020) have brought significant qualitative and quantitative improvements in machine-driven text g…
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior
Md Sultan Al Nahian, Spencer Frazier, Brent Harrison +1
As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally, using only a measure of task performance as f…
Fabula Entropy Indexing: Objective Measures of Story Coherence
Louis Castricato, Spencer Frazier, Jonathan Balloch +1
Automated story generation remains a difficult area of research because it lacks strong objective measures. Generated stories may be linguistically sound, but in many cases suffer…
Playing Text-Based Games with Common Sense
Sahith Dambekodi, Spencer Frazier, Prithviraj Ammanabrolu +1
Text based games are simulations in which an agent interacts with the world purely through natural language. They typically consist of a number of puzzles interspersed with interac…
Reducing Non-Normative Text Generation from Language Models
Xiangyu Peng, Siyan Li, Spencer Frazier +1
Large-scale, transformer-based language models such as GPT-2 are pretrained on diverse corpora scraped from the internet. Consequently, they are prone to generating non-normative t…
Learning Norms from Stories: A Prior for Value Aligned Agents
Spencer Frazier, Md Sultan Al Nahian, Mark Riedl +1
Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans. Traditional approaches to value alignme…
Improving Deep Reinforcement Learning in Minecraft with Action Advice
Spencer Frazier, Mark Riedl
Training deep reinforcement learning agents complex behaviors in 3D virtual environments requires significant computational resources. This is especially true in environments with…