Before revision: Multi-step off-policy reinforcement learninghas achieved great success. However, existing multi-step methods usually impose a fixed prior on the bootstrap steps, while the off-policy methods often require additional correction, suffering from certain undesired effects. In this paper , we propose a novel bootstrapping method , which greedily takes the maximum value among the bootstrapping values with varying steps. The new method has two desired properties:1) it can flexibly adjust the bootstrap step based on the quality of the data and the learned value function;
After revision: Efficiently propagating credit to responsible actions is a central and challenging task in reinforcement learning. To accelerate information propagation, this paper presents a new method has two desired properties:1) it can flexibly adjust the bootstrap step based on the quality of the data and the learned value function;