For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …

Artificial agents have traditionally been trained to maximize reward, which may incentivize
power-seeking and deception, analogous to how next-token prediction in language models …

To interact with humans in the world, agents need to understand the diverse types of
language that people use, relate them to the visual world, and act based on them. While …

In this work, we demonstrate a method for implementing policy iteration using a large
language model. While the application of foundation models to RL has received …

Language agents have shown impressive problem-solving skills within defined settings and
brief timelines. Yet, with the ever-evolving complexities of open-world simulations, there's a …

Text-based games provide an interactive way to study natural language processing. While
deep reinforcement learning has shown effectiveness in developing the game playing …

The development of game agents holds a critical role in advancing towards Artificial General
Intelligence (AGI). The progress of LLMs and their multimodal counterparts (MLLMs) offers …

Text-based games provide a framework for developing natural language understanding and
commonsense knowledge about the world in reinforcement learning based agents. Existing …

In this work, we study reinforcement learning (RL) in solving text-based games. We address
the challenge of combinatorial action space, by proposing a confidence-based self-imitation …

This work presents In-Context Policy Iteration, an algorithm for performing Reinforcement
Learning (RL), in-context, using foundation models. While the application of foundation …