Compulsory Flow Q-Learning: an RL algorithm for robot navigation based on partial-policy and macro-states

Journal of the Brazilian Computer Society

Costa, Anna Helena Reali¹  Universidade de São Paulo, São Paulo, Brasil¹  Silva, Valdinei Freire da¹ 

关键词: machine learning;    reinforcement learning;    abstraction;    partial-policy;    macro-states.;   

DOI  :  10.1007/BF03194507

学科分类：农业科学（综合）

来源: Springer U K

【 摘 要 】

Reinforcement Learning is carried out on-line, through trial-and-error interactions of the agent with the environment, which can be very time consuming when considering robots. In this paper we contribute a new learning algorithm, CFQ-Learning, which uses macro-states, a low-resolution discretisation of the state space, and a partial-policy to get around obstacles, both of them based on the complexity of the environment structure. The use of macro-states avoids convergence of algorithms, but can accelerate the learning process. In the other hand, partial-policies can guarantee that an agent fulfils its task, even through macro-state. Experiments show that the CFQ-Learning performs a good balance between policy quality and learning rate.

【 授权许可】

Unknown