Backpropagation’s main limitation is its need to store intermediate activations, or residuals, during the forward pass, which restricts the depth of trainable networks. This raises a fundamental quest...

The selection committee for the ACM/SIGAI Autonomous Agents Research Award is pleased to announce that Professor Shlomo Zilberstein is the recipient of the 2025 award. Shlomo Zilberstein is Professor…

Efficiently solving problems with large action spaces using A* search has been of importance to the artificial intelligence community for decades. This is because the computation and memory requiremen...

Model-Based Reinforcement Learning (MBRL) has been a powerful tool for visual control tasks. Despite improved data efficiency, it remains challenging to use MBRL to train agents with generalizable per...

Reinforcement Learning Journal (RLJ)

In recent years, Deep Reinforcement Learning (DRL) has emerged as an effective approach to solving real-world tasks. However, despite their successes, DRL-based policies suffer from poor reliability, ...

In standard reinforcement learning settings, agents typically assume immediate feedback about the effects of their actions after taking them. However, in practice, this assumption may not hold true du...

Large language models (LLMs) have recently been used for sequential decision making in interactive environments. However, leveraging environment reward signals for continual LLM actor improvement is n...

Optimizing State Descriptions with Reinforcement Learning for Language Model Actors

It is increasingly common for large language models (LLMs) to be applied as actors in sequential decision making problems in embodied domains such as robotics and games, due to their general world kno...