Evolving population method for real-time reinforcement learning

Reinforcement learning has recently been recognized as a promising means of machine learning, but its applica-bility remains limited in real-time environment due to its short response time, high computational complexity, and instability in learning. Although researchers devised several measures in attempts to press beyond the horizon, the problems consisting of large branching factors with real-time properties still stays unconquered, demanding a new method for reinforcement learning as a whole. In this paper, we propose Evolving Population. This method improves the performance of reinforcement learning by optimizing hyperparameters and available actions. This method uses an iterative structure based on an evolutionary strategy to optimize these elements. We validate the performance of our method in an environment with real-time properties and large branching factors.

Automated summary

Reinforcement learning is promising in machine learning, but its applicability in real-time environment is limited due to short response time, high computational complexity, and learning instability. This paper proposes a new method called Evolving Population, which improves reinforcement learning performance by optimizing hyperparameters and available actions. The method utilizes an iterative structure based on evolutionary strategy to optimize these elements, and its performance is validated in an environment with real-time properties and large branching factors.