An acquiring method of macro-actions in reinforcement learning

Reinforcement learning is a framing of enabling agents to learn from interaction with environments. It has focused generally on Markov decision process (MDP) domains, but a domain may be non-Markovian in the real world. In this paper, we introduce a new description of macro-actions with tree structure in reinforcement learning. The macro-action is an action control structure which provides an agent with control which applies a collection of related microscopic actions as a single action unit. And we propose a simple method for dynamically acquiring macro-actions from the experiences of agents during reinforcement learning process.