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…

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...

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...

In competitive two-agent environments, deep reinforcement learning (RL) methods like Policy Space Response Oracles (PSRO) often increase exploitability between iterations, which is problematic when tr...