Issues

• Review ML-Agents Documentation for Graph Insights Research ML-Agents documentation and related resources to understand best practices and guidelines for graph interpretation. Compare documentation insights with your generated data to validate findings and identify new evaluation criteria. Summarize the findings in a document for easy reference during presentation and further work.

  K33w3/Project2.1#65 · Filter by author EugeniuGh opened on Jan 15, 2025
  

• Graph and Episode Length Analysis Analyze the episode length and all generated graphs to identify trends, anomalies, and areas for improvement. Create a detailed comparison of the performance metrics between different configurations. Propose adjustments to enhance model performance based on insights.

  K33w3/Project2.1#64 · Filter by author EugeniuGh opened on Jan 15, 2025
  

• Train Environments with Updated Parameters Modify .yaml parameters for both environments based on the initial analysis. Train the environments with these new configurations, ensuring thorough logging of episode length, rewards, and other key metrics. Save and backup all training results for future comparisons.

  K33w3/Project2.1#63 · Filter by author EugeniuGh opened on Jan 15, 2025
  

• Integrate Graphs into the Presentation Gather all relevant graphs and images generated during experiments. Begin structuring the presentation with these visual elements, ensuring clear captions and explanations. Identify any missing visual data and schedule additional graph generation if required.

  K33w3/Project2.1#62 · Filter by author EugeniuGh opened on Jan 15, 2025
  

• Develop Grid Search Algorithm for YAML Optimization Design and implement a grid search algorithm to identify the optimal configurations in .yaml files for ML-Agents. Apply the algorithm to both environments and document the outcomes. Include logging and visualization of the search results for easier analysis.

  K33w3/Project2.1#61 · Filter by author EugeniuGh opened on Jan 15, 2025
  

• Define new training parameters optimized for conducting a detailed comparison, ensuring they highlight differences in performance and outcomes effectively.

  K33w3/Project2.1#59 · Filter by author EugeniuGh opened on Jan 10, 2025
  

• Environment Setup

  K33w3/Project2.1#48 · Filter by author EugeniuGh opened on Jan 6, 2025

• Add the appropriate rewards for moving the ball as far away from the teams own goal. Introduce this change in the SoccerEnv file which can be found in the new branch soundSensorChangesAndrei . Document why and what you ve added.

  K33w3/Project2.1#44 · Filter by author K33w3 opened on Nov 28, 2024
  

• Add presentation content within the slides . Do not include full sentences. Make sure to accurately describe the behavior of the agents with both the short ray and the sound sensor implementation.

  K33w3/Project2.1#42 · Filter by author K33w3 opened on Nov 28, 2024
  

• Update the final Report with findings and statistics based on the performance of the model chose. Make sure to measure f1 score and the related metrics.

  K33w3/Project2.1#27 · Filter by author K33w3 opened on Oct 31, 2024
  

• Test

  K33w3/Project2.1#9 · Filter by author RocketFuel7 opened on Sep 26, 2024
  

• Code Modifications

  K33w3/Project2.1#3 · Filter by author K33w3 opened on Sep 12, 2024