Bootstrapped Deep Q Learning

Bootstrapped Deep Q-Learning (BootDQN) enhances the efficiency and robustness of reinforcement learning (RL) by leveraging imitation learning (IL) to improve exploration and reduce the need for extensive trial-and-error. Current research focuses on integrating BootDQN with various model architectures, such as multi-head networks, to improve diversity and generalization across diverse tasks, including robotic control and autonomous navigation. This approach shows promise in improving sample efficiency and performance in complex real-world scenarios, particularly where obtaining sufficient training data is challenging, thereby advancing the applicability of RL in robotics and other domains.