Performance Analysis of Reinforcement Learning Energy Efficiency

Опубликовано: 23 Февраль 2025
на канале: MatlabSimulation. Com

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Title: - Hybrid Reinforcement Learning for Optimal Train Control and Energy Efficiency
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Implementation Plan:
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Step 1: Initially, we Load the Train Monitoring Dataset and Preprocess the data to clean and format it for training the simulator and reinforcement learning models.

Step 2: Next we design the Train Simulation Simulink Model using the Manual Driver Model and Calculate the Train's Dynamics, Position, Speed, Control Systems and Energy Consumption.

Step 3: Next we implement the Custom Reinforcement Learning Environment that represents the Train's Environment, Actions, and Rewards and Customize the Reset, Step, and Reward functions within the environment to match the Train Dynamics.

Step 4: Next we set up a model-based component for the train control and Configure constraints, weights, and other parameters in the Model Predictive Control Toolbox (MPC).

Step 5: Next we Implement the TD3 algorithm for the model-free component in your reinforcement learning environment.

Step 6: Next, we Combine the both model-based component and model-free component to get the Weight Factor as Output.

Step 7: Next , We will Fine Tune the Expert Knowledge System and Heuristic Inference rules into the environment's step function and Optimize the Algorithm used for Training.

Step 8: Finally, the designed system is evaluated with the following plots,
8.1: Convergence Speed
8.2: Exploration Rate
8.3: Energy Efficiency based on Energy Consumption
8.4: Resource Utilization

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Software Requirements:
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1. Tool: Matlab-R2023a
2. OS: Windows-10 (64-bit)
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Note:-
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If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
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#reinforcementlearning
#performanceanalysis
#machinelearning
#datascience
#artificialintelligence
#algorithm
#dataanalysis
#deeplearning
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