Efficient autoscaling in Kubernetes (K8s)-managed in-memory systems like Redis remains a critical challenge, especially under highly dynamic workloads. Traditional threshold-based mechanisms (e.g., HPA) often fail to anticipate sudden demand surges, leading to poor performance and inefficient resource use. We introduce DInos, a Deep Reinforcement Learning (Deep RL) agent enhanced with LSTM layers and transfer learning, designed for proactive and adaptive autoscaling in Kubernetes. As an evolution of our earlier agent DERP, DInos leverages temporal workload modeling and pre-trained policies to generalize across deployments with minimal retraining. DInos utilizes a customizable reward function balancing throughput, latency, resource usage, and pod efficiency. DInos achieves up to 17.3 \(\times \) higher rewards in simulation and a 5.5 \(\times \) improvement in real-world K8s-Redis deployments by forecasting spikes, optimizing pod counts and maintaining low latency, providing a robust autoscaling solution for volatile, cloud-native environments.