#NARENDRA-CONCURRENT-FAULT This is a deep reinforcement learning model used for detecting and classifying faults in Concurrent-Fault scenarios. It deploys a Denoising Auto-Encoder (DAE) for feature extraction, followed by Gated Recurrent Unit (GRU) for feature learning. It implements a Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM) architecture.

The architecture parameters of the proposed model. Layer Size and Parameters, CNN Layer 1 Filters: 8, Kernel Size: 2, Activation: ReLU, Input Shape: [30 × 2], Output Shape: [30 × 8], CNN Layer 2 Filters: 8, Kernel Size: 2, Activation: ReLU, Output Shape: [30 × 8], CNN Layer 3 Filters: 8, Kernel Size: 2, Activation: ReLU, Output Shape: [30 × 8], CNN Layer 4 Filters: 8, Kernel Size: 2, Activation: ReLU, Output Shape: [30 × 8], CNN Layer 5 Filters: 8, Kernel Size: 2, Activation: ReLU, Output Shape: [30 × 8], Batch Normalization Layer 1 Output Shape: [30 × 8], Max Pooling Layer Pool Size: 2, Output Shape: [15 × 8], LSTM Layer 1 Neurons: 64, Activation: ReLU, Output Shape: [15 × 64], LSTM Layer 2 Neurons: 64, Activation: ReLU, Output Shape: [15 × 64], LSTM Layer 3 Neurons: 64, Activation: ReLU, Output Shape: [15 × 64], LSTM Layer 4 Neurons: 64, Activation: ReLU, Output Shape: 64, Batch Normalization Layer 2 Output Shape: 64, Flatten Layer Output Shape: 64, Output Layer Output Shape: 9,

It also incorporates automatic hyper parameter tuning within the following ranges Hyper-parameter ranges and values: CNN Layers: 0–5 LSTM Layers: 0–5 Dense Layers: 0–5 Epochs: 50–900 Max Pooling Layer: 0–1 Drop Layer: 0–2 Batch Normalization Layer: 0–2 Batch Size: 64–150 Learning Rate: 0.001–0.0001

Reinforcement Learning in the provided code:

The code implements episodic Reinforcement Learning (RL) with the REINFORCE algorithm to optimize hyperparameters for a fault detection model. Here's a breakdown of the key elements:

Agent: The agent is the script that samples, trains, and evaluates different model configurations based on hyperparameters.

Environment: The environment is the training process itself. The agent interacts with it by training models with different hyperparameters and receiving rewards based on their validation performance.

State: The state of the environment consists of the current hyperparameter configuration being evaluated.

Action: The action taken by the agent is to randomly sample a new set of hyperparameters based on the previous state and rewards.

Reward: The reward is calculated based on the validation accuracy of the model trained with the current hyperparameters. A higher reward is given for higher accuracy.

REINFORCE Algorithm:

1. Episode Loop: The code runs for a specified number of episodes.
2. Step Loop: In each episode, the agent takes steps within a limit.
3. Sample Hyperparameters: For each step, the agent randomly samples a new set of hyperparameters based on the current values and some noise.
4. Build and Train Model: The agent builds and trains a model using the sampled hyperparameters.
5. Evaluate Model: The model is evaluated on the validation set, and the accuracy is obtained.
6. Calculate Reward: The reward is calculated based on the accuracy, with a custom reward function (example: accuracy - 0.5).
7. Update Hyperparameters: The current hyperparameters are updated using REINFORCE, adjusting their values based on the received reward. This encourages exploring hyperparameters that lead to better rewards.
8. Repeat Steps: Steps 3-7 are repeated for the remaining steps in the episode.

Hyperparameter Search:

• The code defines ranges for various hyperparameters like number of layers, dropout rate, etc.
• It explores different combinations within these ranges during each episode.
• The best performing hyperparameters based on the final reward are identified after all episodes.

Final Model Training:

• The final model is trained with the best hyperparameters found during RL.
• The model is further evaluated on the test set, and metrics like precision, recall, and F1-score are calculated for each fault type.

Overall, the code demonstrates how RL can be effectively used to optimize hyperparameters for a machine learning model, leading to improved performance in this specific fault detection task.

The author is - Ian Mwaniki Kanyi alias King T5M. ALL HAIL T5M.