Leveraging large language models to guide exploration in reinforcement learning enhances training efficiency and convergence.
ETGL-DDPG improves DDPG's performance in sparse reward continuous control tasks by introducing three key innovations: ϵt-greedy for directed exploration, GDRB for efficient experience replay, and longest n-step returns for faster reward propagation.
This research paper introduces SI2E, a novel framework leveraging structural information principles to enhance exploration effectiveness in reinforcement learning agents operating in high-dimensional environments with sparse rewards.