In practical applications, we can rarely assume full observability of a
system's environment, despite such knowledge being important for determining a
reactive control system's precise interaction with its environment. Therefore,
we propose an approach for reinforcement learning (RL) in partially observable
environments. While assuming that the environment behaves like a partially
observable Markov decision process with known discrete actions, we assume no
knowledge about its structure or transition probabilities.
Our approach combines Q-learning with IoAlergia, a method for learning Markov
decision processes (MDP). By learning MDP models of the environment from
episodes of the RL agent, we enable RL in partially observable domains without
explicit, additional memory to track previous interactions for dealing with
ambiguities stemming from partial observability. We instead provide RL with
additional observations in the form of abstract environment states by
simulating new experiences on learned environment models to track the explored
states. In our evaluation, we report on the validity of our approach and its
promising performance in comparison to six state-of-the-art deep RL techniques
with recurrent neural networks and fixed memory.

本文提出了一种基于 Q-learning 和 IoAlergia 的强化学习方法，用于处理部分可观察环境下的控制系统策略生成，将 RL 与学习环境模型相结合以提供抽象的环境状态获取。实验结果表明，该方法在性能表现上优于六种当下的深度 RL 技术。