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Proactive decision making with temporal logic models

This repository contains implementations of the methods presented in manuscript by Chinchali et al., which is currently under review.

To create publication figures

  1. overlaid belief distro:

    • KL solid, H dashed
    • overlay_learning_curve.sh
    • python $CODE_DIR/overlaid_belief_distro.py --KL_results_dir $KL_RESULTS_DIR --H_results_dir $ENTROPY_RESULTS_DIR --config_file $CONF_FILE --output_results_dir $OUTPUT_RESULTS_DIR
  2. overlaid learning curves

    • overlay_learning_curve.sh
    • python -i $CODE_DIR/overlaid_learning_curve.py --KL_results_dir $KL_RESULTS_DIR --entropy_results_dir $ENTROPY_RESULTS_DIR --output_results_dir $OUTPUT_RESULTS_DIR
  3. cart drone images and speed plots

    • drone_data/publication_plots.sh
    • relies on pkls per agent of type:
      • agent_239_scene_gates_video_1.pkl
    • to generate these pkl files call:
    • python idwithtasks/RL/drone_data/single_car_plot.py

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