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. We test our agent's abilities in text games -- challenging benchmarks for evaluating the multi-step reasoning abilities of game agents in grounded, language-based environments. Our experimental study indicates that injecting the actions from these symbolic modules into the action space of a behavior cloned transformer agent increases performance on four text game benchmarks that test arithmetic, navigation, sorting, and common sense reasoning by an average of 22%, allowing an agent to reach the highest possible performance on unseen games. This action injection technique is easily extended to new agents, environments, and symbolic modules.
Code (1)
Tasks
Common Sense ReasoningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games
In this work, we introduce a self-supervised behavior cloning transformer for text games, which are challenging benchmarks for multi-step reasoning in virtual environments. Traditionally, Behavior Cloning Transformers ex…
Robustness of Neurosymbolic Reasoners on First-Order Logic Problems
Recent trends in NLP aim to improve reasoning capabilities in Large Language Models (LLMs), with key focus on generalization and robustness to variations in tasks. Counterfactual task variants introduce minimal but seman…
Large Language Models Are Neurosymbolic Reasoners
A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLM…
Common Sense ReasoningMathtext-based gamesvalidNeurosymbolic Reinforcement Learning and Planning: A Survey
The area of Neurosymbolic Artificial Intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing sub-fields such as Neurosymbolic Deep Learning (Neurosymbolic DL) and Neuro…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)SurveyRelational Neurosymbolic Markov Models
Sequential problems are ubiquitous in AI, such as in reinforcement learning or natural language processing. State-of-the-art deep sequential models, like transformers, excel in these settings but fail to guarantee the sa…
Bayesian Inference