Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are discussed in a new report. According to news reporting originating from Dalian, People's Republic of China, by NewsRx correspondents, research stated, "The trade-off between exploration and exploitation has been one of the main challenges for ensuring sampling efficiency, optimal solution, and transferability of reinforcement learning. Based on the Go-Explore framework, which is currently the most effective framework for the environments with sparse reward, latent go-explore (LGE) can overcome the complexity of manually designing state features." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from the Dalian University of Technology, "However, its state feature space is not effective enough for measuring the sampling density, and the exploration mode with a single state as a unit is inefficient. To this end, this paper proposes the LGE with the state area as a unit, named ALGE, which can encode the real environment distance into the state feature space and realize the exploration mode with the state area as a unit to further improve exploring efficiency. The proposed ALGE is verified by a series of experiments in multiple hard-exploration environments including a continuous-maze environment, a robot environment and two Atari environments."
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