Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

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 …

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 …

The end-to-end learning ability of self-driving vehicles has achieved significant milestones
over the last decade owing to rapid advances in deep learning and computer vision …

One major challenge in reinforcement learning (RL) is the large amount of steps for the RL
agent needs to converge in the training process and learn the optimal policy, especially in …

Reinforcement Learning has shown success in a number of complex virtual environments.
However, many challenges still exist towards solving problems with natural language as a …

Reward design for reinforcement learning agents can be difficult in situations where one not
only wants the agent to achieve some effect in the world but where one also cares about …

Imaginative play is an area of creativity that could allow robots to engage with the world
around them in a much more personified way. Imaginary play can be seen as taking real …

This survey summarises the most recent methods for building and assessing helpful, honest,
and harmless neural language models, considering small, medium, and large-size models …