Smart Start and HER for a Directed and Persistent Reinforcement Learning Exploration in Discrete Environment

Reinforcement learning (RL) solves sequential decision making problems through trial and error, through experiences can be amassed to achieve goals and increase the accumulative rewards. Exploration-exploitation dilemma is a critical challenge in reinforcement learning, particularly environments with misleading or sparse rewards which have shown difficulties to construct a suitable exploration strategy. In this paper a framework for Smart Start (SS) and Hindsight experience replay (HER) is developed to improve the performance of SS and make the exploration more directed especially in the early episodes. The framework Smart Start and Hindsight experience replay (SS+HER) was studied in discrete maze environment with sparse rewards. The results reveal that the framework doubles the rewards at the early episodes and decreases the time of the agent to reach the goal.

@article{Alrakh2020,
  title     = {Smart Start and HER for a Directed and Persistent Reinforcement Learning Exploration in Discrete Environment},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {11},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Heba Alrakh and Muhammad Fahmi Miskon and Rozilawati Mohd Nor},
  doi       = {10.14569/IJACSA.2020.0111117},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111117}
}