Install

pip install -r requirements.txt 

Run

python3 plot_all_one.py plot_data/builtin_results.csv --vs-builtin

Generates the plots vs the builtin agent

python3 plot_all_one.py plot_data/all_out.txt  

Generates the plots vs the random agent

Plots can be found near the data file, i.e. plot_data/all_out.txt.png

python3 experiment_train.py boxing_v1 nfsp_rainbow  

Basic local hyperparameter search

python3 hparam_search.py --env boxing_v1 --local 

Evaluate with fixed checkpoint

python3 experiment_eval.py boxing_v1 nfsp_rainbow [checkpoint_num] [path/to/checkpoint/dir] 

For example:
python3 experiment_eval.py pong_v2 000500000 checkpoint/shared_rainbow/pong_v2/RB1000000_F50000000.0_S1636751414

Run hyperparameter search on Slurm HPC

• Generate run command file:

python gen_hparam_search_cmds.py --study-name [name] \ 
    --db-password [password] --db-name [name] --num-concurrent [number]

• Start Slurm job (ensure enough resources are given!):

python cml.py hparam_search_cmds.txt --conda ma-ale --min_preemptions \
    --gpus [num] --mem [48*num_gpus] > optuna.log 2>&1 

Files Overview

• Environment code
  • all_envs.py
    • contains list of pettingzoo environments that should be trained
  • env_utils.py
    • contains environment preprocessing code
  • my_env.py
    • MultiagentPettingZooEnv wrapper needed for NFSP
• Policy code (each one of these has a function that makes a trainable ALL agent).
  • nfsp.py
  • shared_ppo.py
  • shared_rainbow.py
  • shared_utils.py
  • shared_vqn.py (not used/working!)
  • independent_rainbow.py
  • independent_ppo.py
  • model.py
    • some experimental models to use for policies
• Training code
  • experiment_train.py
    • trains agent returned by policy code
  • gen_train_runs.py
    • Generates many command line calls to experiment_train.py so that many experiments can be run with Slurm, Kabuki, or another job execution service.
• Evaluation code
  • experiment_eval.py
    • evaluates agent returned by checkpoint
    • can evaluate vs random agent, vs builtin agent (on specific environments), or vs trained opponent.
  • ale_rand_test.py
    • evaluates random agent vs random agent on all the environments, reports the results in a json file
  • ale_rand_test_builtin.py
    • evaluates random agent vs builtin agent on all the environments, reports the results in a json file
  • generate_evals.py
    • generates many calls to experiment_eval.py so that the evaluation jobs can be run with Slurm, Kabuki, or another job execution service
• Plotting code
  • plot_all_one.py
    • Looks at input csv file, specific random data file inside plot_data folder, and generates a plots with the results
• Hyperparameter search code
  • gen_hparam_search_cmds.py
    • Writes a file with many calls to "hparam_search.py", currently one per environment
    • Set up like this to use the "cml.py" SLURM tool to easily run batch array jobs on CML
  • hparam_search.py
    • Uses optuna with provided study name (allowing distributed) to optimize over normalized env scores
    • This is doing asynchronous optimization between envs, syncing to shared SQL result database