Advances, Systems and Applications
Table 3 Learning parameters of the DDQNEC algorithm
From: Optimizing task offloading and resource allocation in edge-cloud networks: a DRL approach
Definitions and description | Values |
|---|---|
Dense-layer setup (Hidden) | 256 |
N-step for Q-learning | 1 |
Replay Buffer Capacity ( Size of the replay buffer) | 10,000 |
The target network smoothly copies the parameter | 0.005 |
Initial epsilon (Exploration) | 1.0 |
Final epsilon (Exploration) | 0.1 |
Target synchronization interval training steps | 1000 |
Learning rate | 0.001 |
Training batch size | 32 |
Discount factor | 0.99 |