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RL for navigation

Overview

This is a repo for training reinforcement-learning agents is a gridworld, such that agents optimize for the shortest integrated Euclidean distance to rewards. Currently supported models:

  • Tabular Q learning
  • Tabular SARSA
  • State-action Successor Representation
  • Tile coding (with Q learning)
  • Hierarchical state space (with Q learning or SARSA)
  • Model-based tree search

Usage

  • Install the repo from the directory ...\Euclidean_Gridworld_RL using pip install -e .
  • Modify the config files as needed in experiments/suites/...
  • Run an experiment, e.g. with the command python run.py --mode parallel --seeds 10 --config_path suites/a_star/config.yaml --config_changes config_changes.py
  • Visualize results, e.g. with the command python post_process.py --plot_t --results_folder results\...