Advances, Systems and Applications
Table 4 Fine-tuned baseline approaches: we train DQN, Double-DQN, CEM based approaches as baselines of our proposed MLR-LC-DRLO
Fine-tuned RL Approaches | ||||||
|---|---|---|---|---|---|---|
Parameters | NN Layers | Replay Buffer Size | Optimizer | \(\rho\) | Learning Rate | Activation Function |
Baseline Approaches | Â | Â | Â | Â | Â | Â |
DQN | 4 | − | Adam | 0.95 | 1e-3 | ReLU, Softmax |
Double-DQN | 3 | 500 | Adam | 0.95 | 1e-3 | ReLU, Softmax |
CEM | 3 | − | Adam | 0.95 | 1e-3 | ReLU, Softmax |