Kubernetes is a representative open-source software for container orchestration, playing a crucial role in monitoring a nd managing resources allocated to containers. As container environments become prevalent, security threats targeting con tainers continue to rise, with resource exhaustion attacks being a prominent example. These attacks involve distributing m alicious crypto-mining software in containerized form to hijack computing resources, thereby affecting the operation of the host and other containers that share resources. Previous research has focused on detecting resource depletion attacks, so t echnology to respond when attacks occur is lacking. This paper proposes a reinforcement learning-based dynamic resource management framework for detecting and responding to resource exhaustion attacks and malicious containers running in Kubernetes environments. To achieve this, we define the environment's state, actions, and rewards from the perspective of responding to resource exhaustion attacks using reinforcement learning. It is expected that the proposed methodology will contribute to establishing a robust defense against resource exhaustion attacks in container environments