Dynamic consolidation techniques optimize resource utilization and reduce energy consumption in Cloud data centers. They should consider the variability of the workload to decide when idle or underutilized hosts switch to sleep mode in order to minimize energy consumption. In this paper, we propose a Reinforcement Learning-based Dynamic Consolidation method (RL-DC) to minimize the number of active hosts according to the current resources requirement. The RL-DC utilizes an agent to learn the optimal policy for determining the host power mode by using a popular reinforcement learning method. The agent learns from past knowledge to decide when a host should be switched to the sleep or active mode and improves itself as the workload changes. Therefore, RL-DC does not require any prior information about workload and it dynamically adapts to the environment to achieve online energy and performance management. Experimental results on the real workload traces from more than a thousand PlanetLab virtual machines show that RL-DC minimizes energy consumption and maintains required performance levels.