A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback

Understand

Reinforcement learning (RL) is one of the active fields in machine learning, demonstrating remarkable potential in tackling real-world challenges.

• Despite its promising prospects, this methodology has encountered with issues and challenges, hindering it from achieving the best performance.
• In particular, these approaches lack decent performance when navigating environments and solving tasks with large observation space, often resulting in sample-inefficiency and prolonged learning times.
• This issue, commonly referred to as the curse of dimensionality, complicates decision-making for RL agents, necessitating a careful balance between attention and decision-making.