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 …

Text-based reinforcement learning agents have predominantly been neural network-based
models with embeddings-based representation, learning uninterpretable policies that often …

Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring
reinforcement learning (RL) agents to combine natural language understanding with …

Reinforcement learning with natural language action space often suffers from the curse of
dimensionality due to the combinatorial nature of the natural language. Previous research …

The ability to reason, plan and solve highly abstract problems is a hallmark of human
intelligence. Recent advancements in artificial intelligence, propelled by deep neural …

The completeness of knowledge graphs is critical to their effectiveness across various
applications. However, existing knowledge graph completion methods face challenges such …

Text-based game environments are challenging because agents must deal with long
sequences of text, execute compositional actions using text and learn from sparse rewards …

Text-based games present an exciting test-bed for reinforcement learning algorithms in the
natural language environment. In these adventure games, an agent must learn to interact …

Real-world sequential decision making is characterized by sparse rewards and large
decision spaces, posing significant difficulty for experiential learning systems like $\textit …

Text-based reinforcement learning involves an agent interacting with a fictional environment
using observed text and admissible actions in natural language to complete a task. Previous …