Added waste management through rl techniques #1152
Conversation
|
Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊 |
|
@Niketkumardheeryan Please reassign this issue with labels ,Thank you |
|
@Niketkumardheeryan Please review as GSSoC is going to end by 2 days |
|
@Panchadip-128 add your dataset used details and add your name in .ipynb file , remove unnecessary files |
|
@Niketkumardheeryan I have added the necessary dataset informations and keys to be added. Please note that for a RL model like the waste management system using epsillon decay , typically no predefined dataset is required as the agent learns by interacting with a simulated environment. |
The project aims to develop a reinforcement learning (RL) agent to optimize waste collection in a simulated environment, minimizing overflow events and improving efficiency.
Environment and State Representation:
The state is represented by four features: Waste Level: Current waste level (0 to 1) Time of Day: A random value representing the time (0 to 24 hours) Weather Condition: A random value (0 to 1) indicating the weather Distance to Collection Point: A random value (0 to 10) representing the distance to the waste collection point.
Action Space:
The agent can choose between two actions: Wait (0): Do not collect waste. Collect Waste (1): Proceed with waste collection.
Reward Structure:
The reward system is designed to encourage efficient waste collection: +10 for timely collection when the waste level exceeds the threshold. -5 for premature collection when the waste level is below the threshold. -1 for each time step to penalize waiting.
Training Process:
The agent is trained over 100 episodes, where each episode simulates a series of time steps (up to 20) where the agent makes decisions based on the current state. The agent learns from experience using a replay memory and updates its policy through Q-learning.
Evaluation Metrics:
Performance is evaluated using: Average Reward per Episode: Measures the effectiveness of the agent's actions. Epsilon Decay: Tracks the exploration rate, indicating how the agent balances exploration vs. exploitation. Overflow Events: Counts occurrences when the waste level exceeds the maximum capacity as per previous updation.
Visualization:
The results are visualized using Matplotlib to plot: Average rewards per episode, showing the agent's learning progression and rewards gained on successfull execution and implementation of a specified condition Epsilon decay over episodes, illustrating the shift from exploration to exploitation. Overflow events per episode, highlighting improvements in waste management techniques