Exploration in Deep Reinforcement Learning: A Survey
It provides a comprehensive overview for researchers working on reinforcement learning, but it is incremental as it synthesizes existing methods without introducing new ones.
This survey reviews exploration techniques in deep reinforcement learning, focusing on methods to address sparse reward problems by categorizing approaches and comparing them based on complexity, computational effort, and performance.
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly. In such a scenario, it is challenging for reinforcement learning to learn rewards and actions association. Thus more sophisticated exploration methods need to be devised. This review provides a comprehensive overview of existing exploration approaches, which are categorized based on the key contributions as follows reward novel states, reward diverse behaviours, goal-based methods, probabilistic methods, imitation-based methods, safe exploration and random-based methods. Then, the unsolved challenges are discussed to provide valuable future research directions. Finally, the approaches of different categories are compared in terms of complexity, computational effort and overall performance.