Imitation Bootstrapped Reinforcement Learning: A Novel Approach for Sample-Efficient RL with Demonstrations

Imitation Bootstrapped Reinforcement Learning (IBRL) proposes a novel framework that combines imitation learning (IL) and reinforcement learning (RL) to achieve sample-efficient RL with expert demonstrations. By integrating IL policies in both exploration and training phases, IBRL accelerates learning and outperforms prior methods.