Advances in Hierarchical Reinforcement Learning and Optimization

The field of artificial intelligence is witnessing significant developments in hierarchical reinforcement learning (HRL) and optimization techniques. Researchers are exploring ways to discover and exploit temporal structure in complex environments, which is a crucial aspect of HRL. The discovery of temporal structure can help agents make better decisions and improve their performance in open-ended environments. Additionally, there is a growing interest in understanding the connections between different optimization paradigms, such as zeroth-order optimization and policy optimization. These advances have the potential to improve the performance of AI agents in a wide range of applications. Noteworthy papers in this area include:

  • Zeroth-Order Optimization is Secretly Single-Step Policy Optimization, which establishes a fundamental connection between zeroth-order optimization and policy optimization.
  • Zero-Shot Reinforcement Learning Under Partial Observability, which explores the performance of zero-shot RL methods under partial observability and proposes memory-based architectures as a remedy.

Sources

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning

Zeroth-Order Optimization is Secretly Single-Step Policy Optimization

Reward Models in Deep Reinforcement Learning: A Survey

Zero-Shot Reinforcement Learning Under Partial Observability

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