RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with Explanation
arxiv(2024)
摘要
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
is to construct a new initial state distribution that combines both the default
initial states and critical states identified through explanation methods,
thereby encouraging the agent to explore from the mixed initial states. Through
careful design, we can theoretically guarantee that our refining scheme has a
tighter sub-optimality bound. We evaluate RICE in various popular RL
environments and real-world applications. The results demonstrate that RICE
significantly outperforms existing refining schemes in enhancing agent
performance.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要