Building agents based on tree-search planning capabilities with learned models has
achieved remarkable success in classic decision-making problems, such as Go and Atari …

Reinforcement Learning (RL) has achieved tremendous development in recent years, but
still faces significant obstacles in addressing complex real-life problems due to the issues of …

We investigate the use of transformer sequence models as dynamics models (TDMs) for
control. We find that TDMs exhibit strong generalization capabilities to unseen …

Humans and animals can learn new skills after practicing for a few hours, while current
reinforcement learning algorithms require a large amount of data to achieve good …

We tackle the problem of policy learning from expert demonstrations without a reward
function. A central challenge in this space is that these policies fail upon deployment due to …

Autonomous driving holds promise for increased safety, optimized traffic management, and
a new level of convenience in transportation. While model-based reinforcement learning …

Adversarial imitation learning (AIL) is a powerful method for automated decision systems
due to training a policy efficiently by mimicking expert demonstrations. However, implicit bias …

Monte Carlo tree search (MCTS) has been gaining increasing popularity, and the success of
AlphaGo has prompted a new trend of incorporating a value network and a policy network …

Although reinforcement learning has seen tremendous success recently, this kind of trial-
and-error learning can be impractical or inefficient in complex environments. The use of …

The drive for efficiency and safety in construction has boosted the role of robotics and
automation. However, complex tasks like welding and pipe insertion pose challenges due to …