RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation

arXiv:2405.03064v1 Announce Type: new
Abstract: Deep reinforcement learning (DRL) is playing an increasingly important role in real-world applications. However, obtaining an optimally performing DRL agent for complex tasks, especially with sparse rewards, remains a significant challenge. The training of a DRL agent can be often trapped in a bottleneck without further progress. In this paper, we propose RICE, an innovative refining scheme for reinforcement learning that incorporates explanation methods to break through the training bottlenecks. The high-level idea of RICE …

abstract agent applications arxiv bottlenecks breaking challenge cs.ai cs.cr cs.lg however playing reinforcement reinforcement learning role tasks through training type world