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Found 32 papers, 22 papers with code
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Unsupervised State Representation Learning in Atari
State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of tasks.
Neural Autoregressive Distribution Estimation
We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density estimation.
ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
ALFWorld enables the creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, corresponds directly to concrete, visually grounded actions.
Interactive Fiction Games: A Colossal Adventure
A hallmark of human intelligence is the ability to understand and communicate with language.
ScienceWorld: Is your Agent Smarter than a 5th Grader?
We present ScienceWorld, a benchmark to test agents' scientific reasoning abilities in a new interactive text environment at the level of a standard elementary school science curriculum.
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Thus, they can be seen as stochastic language layers in a language network, where the learnable parameters are the natural language prompts at each layer.
Graph Policy Network for Transferable Active Learning on Graphs
Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields.
TextWorldExpress: Simulating Text Games at One Million Steps Per Second
Text-based games offer a challenging test bed to evaluate virtual agents at language understanding, multi-step problem-solving, and common-sense reasoning.
Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Playing text-based games requires skills in processing natural language and sequential decision making.
Z-Forcing: Training Stochastic Recurrent Networks
Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech.
Counting to Explore and Generalize in Text-based Games
We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments.
An Infinite Restricted Boltzmann Machine
We present a mathematical construction for the restricted Boltzmann machine (RBM) that doesn't require specifying the number of hidden units.
Behavior Cloned Transformers are Neurosymbolic Reasoners
In this work, we explore techniques for augmenting interactive agents with information from symbolic modules, much like humans use tools like calculators and GPS systems to assist with arithmetic and navigation.
ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games
In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks.
Building Dynamic Knowledge Graphs from Text-based Games
We are interested in learning how to update Knowledge Graphs (KG) from text.
Collecting Interactive Multi-modal Datasets for Grounded Language Understanding
Human intelligence can remarkably adapt quickly to new tasks and environments.
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions
The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy.
Lessons learned in multilingual grounded language learning
Recent work has shown how to learn better visual-semantic embeddings by leveraging image descriptions in more than one language.
TextWorld: A Learning Environment for Text-based Games
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games.
Towards Solving Text-based Games by Producing Adaptive Action Spaces
To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success.
Revisiting the Hierarchical Multiscale LSTM
Hierarchical Multiscale LSTM (Chung et al., 2016a) is a state-of-the-art language model that learns interpretable structure from character-level input.
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration.
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language
In order to study how agents can be taught to query external knowledge via language, we first introduce two new environments: the grid-world-based Q-BabyAI and the text-based Q-TextWorld.
Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents
In this extended abstract we discuss the opportunities and challenges of studying intrinsically-motivated agents for exploration in textual environments.
A Song of Ice and Fire: Analyzing Textual Autotelic Agents in ScienceWorld
We show the importance of selectivity from the social peer's feedback; that experience replay needs to over-sample examples of rare goals; and that following self-generated goal sequences where the agent's competence is intermediate leads to significant improvements in final performance.
Augmenting Autotelic Agents with Large Language Models
Without relying on any hand-coded goal representations, reward functions or curriculum, we show that LMA3 agents learn to master a large diversity of skills in a task-agnostic text-based environment.
Policy Improvement using Language Feedback Models
We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instruction following.
Language-guided Skill Learning with Temporal Variational Inference
We present an algorithm for skill discovery from expert demonstrations.
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following
Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions.
