Cloud Reasoning Model-Based Exploration for Deep Reinforcement Learning

Reinforcement learning which has self-improving and online learning properties gets the policy of tasks through the interaction with environment. But the mechanism of trial-and-error usually leads to a large number of training episodes. Knowledge includes human experience and the cognition of environment. This paper tries to introduce the qualitative rules into the reinforcement learning, and represents these rules through the cloud reasoning model. It is used as the heuristics exploration strategy to guide the action selection. Empirical evaluation is conducted in OpenAI Gym environment called CartPole-v2 and the result shows that using exploration strategy based on the cloud reasoning model significantly enhances the performance of the learning process.