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Panchadip-128
Is your feature request related to a problem? Please describe.
Describe the solution you'd like 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
Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.
Approach to be followed (optional) A clear and concise description of the approach to be followed.
Additional context Add any other context or screenshots about the feature request here.