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Induction of Subgoal Automata for Reinforcement Learning

Implementation of the ISA (Induction of Subgoal Automata) algorithm presented in [Furelos-Blanco et al., 2020] and [Furelos-Blanco et al., 2021].

  1. Installation
    1. Install Python packages
    2. Install ILASP and clingo binaries
  2. Run the algorithm
  3. Generation of configuration files
  4. Plot the learning curves
  5. Collect learning statistics
  6. Reproducibility of the paper results
  7. References

Installation

The code only runs on Linux or MacOS computers with Python 3. Firstly, you have to download the repository which can be done with the following command.

git clone https://github.com/ertsiger/induction-subgoal-automata-rl.git

The following subsections describe the steps for installing the required Python packages and the binaries related to the Inductive Logic Programming system.

Install Python packages

To install the required Python packages to run our code, you can use pip with the following command:

cd induction-subgoal-automata-rl
pip install -r requirements.txt

Note that one of the requirements is the package in the gym-subgoal-automata repository (link). We use the environments implemented in that repository.

We recommend you to use a virtual environment since the requirements of our installation may affect your current installation.

Install ILASP and clingo binaries

The cloned repository does not include the binaries for the ILASP inductive logic programming system. Therefore, you have to download the binaries from the following websites and then copy the ILASP and clingo binaries into the bin folder:

Alternatively, you can run the install_binaries.sh, which will download and put the files in the bin folder for you.

Running the algorithm

The ISA algorithm can be executed easily by running the run_isa.py script:

python3 run_isa.py algorithm config_file

where

  • algorithm can be either hrl or qrm; and
  • config_file is the path to a JSON configuration file containing the settings with which ISA is executed.

We provide a script that automatically generates a set of configuration files using some default parameters as well as other parameters specified through the command line (see this section). Alternatively, we provide example configuration files in the config/examples folder. In case you want to manually modify the configuration files, you can find more details about them in the following code files:

  • LearningAlgorithm - Basic learning algorithm fields.
  • ISAAlgorithmBase - Specifies basic scheme for interleaving. Inherits from Learning Algorithm.
  • ISAAlgorithmHRL - Interleaving algorithm using Hierarchical Reinforcement Learning (HRL). Inherits from ISAAlgorithmBase.
  • ISAAlgorithmQRM - Interleaving algorithm using Q-Learning for Reward Machines (QRM). Inherits from ISAAlgorithmBase.

Generation of configuration files

The config/config_generator script allows to create files similar to the ones we used in our experiments. It is executed as follows:

python -m config.config_generator [--maximum_episode_length MAXIMUM_EPISODE_LENGTH]
                                  [--num_tasks NUM_TASKS] [--seed SEED]
                                  [--interleaved_learning]
                                  [--use_restricted_observables]
                                  [--max_disj_size MAX_DISJ_SIZE] [--learn_acyclic]
                                  [--symmetry_breaking_method SYMMETRY_BREAKING_METHOD]
                                  [--use_compressed_traces]
                                  [--ignore_empty_observations]
                                  [--prioritize_optimal_solutions]
                                  [--rl_guidance_method RL_GUIDANCE_METHOD]
                                  [--avoid_learning_negative_only_formulas]
                                  [--environments ENVIRONMENTS [ENVIRONMENTS ...]]
                                  [--use_gpu] [--multitask]
                                  domain algorithm num_runs root_experiments_path
                                  experiment_folder_name

where:

  • domain can be officeworld, craftworld or waterworld.
  • algorithm can be hrl or qrm.
  • num_runs is the number of different runs (one folder for each run will be generated, each using a different seed).
  • root_experiments_path is the path where the experiment folder (below) is created.
  • experiment_folder_name is the name of the folder containing the experiments.
  • --maximum_episode_length is used to specify the maximum number of steps per episode.
  • --num_tasks is the size of the MDP set used to learn the automata.
  • --seed is the starting seed used to randomly initialize each of the tasks in the MDP set (the first task uses this value, the second task uses this value plus one, ...).
  • --interleaved_learning indicates whether an automaton is learned in an interleaved manner (if false, the target automaton for the tasks is used and no automata learning occurs).
  • --use_restricted_observables indicates whether only the observables relevant to the task at hand should be used;
  • --max_disj_size is the maximum number of edges from one state to another.
  • --learn_acyclic indicates whether to add constraints to enforce the automaton to be acyclic.
  • --symmetry_breaking_method is the name of the symmetry breaking method (if it is not specific, no symmetry breaking method is used):
    • bfs - direct translation from the SAT encoding into ASP.
    • bfs-alternative - ASP encoding of the symmetry breaking method alternative to the direct translation from SAT.
    • increasing-path - Method used in the AAAI-20 paper (only works for acyclic automata).
  • --use_compressed_traces compresses contiguous equal observations in a trace into a single observation.
  • --ignore_empty_observations removes empty observations from a trace.
  • --prioritize_optimal_solutions adds weak constraints to rank equall optimal solutions found by ILASP (still experimental).
  • --rl_guidance_method is the name of the method uses to provide extra reward signals to the learner (if left empty, no method is used):
    • In the case of hrl, use the name pseudorewards (there is only one method).
    • In the case of qrm, you can use max_distance (currently working for acyclic graphs only) and min_distance.
  • --avoid_learning_negative_only_formulas indicates whether to avoid learning formulas formed only by negated observables.
  • --environments is a list of the environments for which the configuration files are generated. You should use the aliases shown in run_isa.py (e.g., coffee, coffee-mail). They all must correspond to the domain specified before.
  • --use_gpu indicates whether to use the GPU when deep learning is applied (e.g., in WaterWorld tasks).
  • --multitask indicates whether to enable the multi-task setting (i.e., learn a policy and an automaton for each environment).

Plot the learning curves

To plot the learning curves, you can run the plot_curves.py script as follows:

plot_curves.py [--max_episode_length MAX_EPISODE_LENGTH]
               [--plot_task_curves] [--use_greedy_traces]
               [--greedy_evaluation_frequency GREEDY_EVALUATION_FREQUENCY]
               [--use_tex] [--window_size WINDOW_SIZE]
               [--plot_title PLOT_TITLE]
               config num_tasks num_runs num_episodes

where:

  • config is a JSON configuration file with the paths to the folders generated by the run_isa.py script. More details below.
  • num_tasks is the number of tasks specified in the JSON configuration file given to the run_isa.py script.
  • num_runs is number of runs specified in the JSON configuration file given to the run_isa.py script.
  • num_episodes is the number of episodes to plot.
  • --max_episode_length is the maximum number of steps that can be run per episode (it should be equal to the value given in the JSON configuration file).
  • --plot_task_curves indicates whether to plot the learning curves for each of the tasks used to learn the automaton. If not specified, only the average curve across tasks and runs will be shown.
  • --use_greedy_traces indicates whether to use the evaluations of the greedy policy to plot the curves. Else, the results obtained by the behavior policy are used (in our case, epsilon-greedy). This will only work if greedy evaluation was enabled in ISA's execution.
  • --greedy_evaluation_frequency indicates every how many episodes is the greedy policy evaluated (should have the same value as in the configuration file of ISA).
  • --use_tex indicates whether to use TeX to label the axis and the labels in the plot.
  • --window_size is the size of the sliding window that averages the reward and the number of steps to make curves smoother.
  • --plot_title is the title of the plot.

The configuration file is formed by a list of objects: one for each curve. Each object has three fields:

  • label - The name that will appear in the legend.
  • folders - A list of paths to the folders where the results of the algorithm execution are stored. There is a folder for each run.
  • colour - The colour of the learning curve in hexadecimal format.

The following is an example of a JSON configuration file:

[
  {
    "label": "HRL",
    "folders": [
      "hrl-coffee-run1",
      "hrl-coffee-run2"
    ],
    "colour": "#AAAA00"
  },
  {
    "label": "HRL-G",
    "folders": [
      "hrl-g-coffee-run1",
      "hrl-g-coffee-run2"    
    ],
    "colour": "#EEDD88"
  }
]

Then, if the number of tasks is 100, the number of runs is 2 and we want to plot 1000 episodes, the script would be executed as:

python -m plot_utils.plot_curves.py config.json 100 2 1000

Collect learning statistics

The collect_stats.py script produces JSON files containing a summary of the results obtained from the folders generated by run_isa.py. The script can be run as follows:

python -m result_processing.collect_stats.py config_file output_file

The configuration file contains a JSON object with one item per setting. Each item consists of a list of result folders generated by run_isa.py. There should be one folder for each run of that setting. The following is an example file:

{
  "HRL": [
    "hrl-coffee-run1",
    "hrl-coffee-run2"
  ],
  "HRL-G": [
    "hrl-g-coffee-run1",
    "hrl-g-coffee-run2"
  ]
}

The output is a JSON file with the following fields for each of the settings in the input. All of them provide the average and the standard error across runs except where noted.

  • num_examples - Total number of examples.
  • num_goal_examples - Number of goal examples.
  • num_dend_examples - Number dead-end examples.
  • num_inc_examples - Number incomplete examples.
  • absolute_time - Total running time (reinforcement learning + automata learning).
  • num_completed_runs - Number of runs that have been successfully completed (i.e., without timeouts).
  • num_found_goal_runs - Number of runs for which the goal has been found at least once (i.e., automata learning has happened).
  • ilasp_total_time - ILASP running time.
  • ilasp_percent_time - Fraction of time during which ILASP runs with respect to ISA's total running time.
  • ilasp_last_time - ILASP running time for the last automaton.
  • avg_time_per_automaton - Average and standard error of the time needed for each intermediate automaton solution.
  • max_example_length - Length of the longest example across runs.
  • example_length - Average and standard deviation of the example length taking into account the examples from all tasks.
  • example_length_goal - Average and standard deviation of the goal example length taking into account the examples from all tasks.
  • example_length_dend - Average and standard deviation of the dead-end example length taking into account the examples from all tasks.
  • example_length_inc - Average and standard deviation of the incomplete example length taking into account the examples from all tasks.

Reproducibility of the paper results

The experiments ran on 3.40GHz Intel Core i7-6700 processors using Python 3.6.9. The requirements.txt file contains the versions of the required Python packages.

We provide scripts that generate all the experiments described in the paper as well as scripts that generate plots and statistics reports from them. The folder paper-experiments in the root of the repository has a folder called results containing JSON files for generating plots and reports. We now describe how to create the experiments and then generate results out from them.

To generate the experiments, just go to the src folder and run the following command:

sh config/generate_experiments.sh

This will generate an experiments folder inside the paper-experiments folder introduced before. This folder contains the configuration files of all the experiments referenced in the paper. There are quite a lot of experiments to run, so you might want to comment some of the lines at the bottom of the config/generate_experiments.sh file. Furthermore, take into account that checkpointing is enabled, so a lot of heavy files will be generated during the execution of the experiments.

Once all the experiments have run, you can run the following command:

sh config/get_results.sh

This will fill the paper-experiments/results folder with plots and statistics reports. Again, you can comment some of the lines at the bottom of the file if not all experiments have run. However, note that the results for some experiments depend on the results of others. For example, HRL with guidance is not rerun in the set of experiments where the different RL algorithms are evaluated in OfficeWorld.

Since it is costly to run all these experiments, we recommend you to use the configuration generator introduced here if you want to test the method with some specific experiments.

References

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Implementation of "Induction of Subgoal Automata for Reinforcement Learning" (AAAI-20) and "Induction and Exploitation of Subgoal Automata for Reinforcement Learning" (JAIR).

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