def test_ltft_ipd():
from marltoolbox.experiments.rllib_api.ltft_various_env import main
ray.shutdown()
tune_analysis_self_play, tune_analysis_against_opponent = main(
debug=False,
env="IteratedPrisonersDilemma",
train_n_replicates=1,
against_naive_opp=True)
print_metrics_available(tune_analysis_self_play)
check_learning_achieved(
tune_results=tune_analysis_self_play,
min_=-42)
check_learning_achieved(
tune_results=tune_analysis_self_play,
min_=0.9,
metric="custom_metrics.CC_freq/player_row_mean")
check_learning_achieved(
tune_results=tune_analysis_self_play,
min_=0.9,
metric="custom_metrics.CC_freq/player_col_mean")
print_metrics_available(tune_analysis_against_opponent)
check_learning_achieved(
tune_results=tune_analysis_against_opponent,
max_=-75)
check_learning_achieved(
tune_results=tune_analysis_against_opponent,
min_=0.9,
metric="custom_metrics.DD_freq/player_row_mean")
check_learning_achieved(
tune_results=tune_analysis_against_opponent,
min_=0.9,
metric="custom_metrics.DD_freq/player_col_mean")
def test_ppo_asym_coin_game():
from marltoolbox.examples.rllib_api.ppo_coin_game import main
ray.shutdown()
tune_analysis = main(debug=False, stop_iters=200)
print_metrics_available(tune_analysis)
check_learning_achieved(
tune_results=tune_analysis, min_=15)
check_learning_achieved(
tune_results=tune_analysis,
min_=0.30,
metric="custom_metrics.pick_speed/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.30,
metric="custom_metrics.pick_speed/player_blue_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.40,
max_=0.60,
metric="custom_metrics.pick_own_color/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.40,
max_=0.60,
metric="custom_metrics.pick_own_color/player_blue_mean")
def test_amtft_coin_game():
from marltoolbox.experiments.rllib_api.amtft_various_env import main
ray.shutdown()
tune_analysis_per_welfare, analysis_metrics_per_mode = main(
debug=False, train_n_replicates=1, filter_utilitarian=False,
env="CoinGame")
for welfare_name, tune_analysis in tune_analysis_per_welfare.items():
print("welfare_name", welfare_name)
print_metrics_available(tune_analysis)
check_learning_achieved(
tune_results=tune_analysis,
min_=40)
check_learning_achieved(
tune_results=tune_analysis,
min_=0.25,
metric="custom_metrics.pick_speed/player_red_mean")
def test_pg_ipd():
from marltoolbox.examples.rllib_api.pg_ipd import main
# Restart Ray defensively in case the ray connection is lost.
ray.shutdown()
tune_analysis = main(debug=False)
print_metrics_available(tune_analysis)
check_learning_achieved(
tune_results=tune_analysis,
max_=-75)
check_learning_achieved(
tune_results=tune_analysis,
min_=0.9,
metric="custom_metrics.DD_freq/player_row_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.9,
metric="custom_metrics.DD_freq/player_col_mean")
def test_ltft_coin_game():
from marltoolbox.experiments.rllib_api.ltft_various_env import main
ray.shutdown()
tune_analysis_self_play, tune_analysis_against_opponent = main(
debug=False, env="CoinGame", train_n_replicates=1,
against_naive_opp=True)
print_metrics_available(tune_analysis_self_play)
check_learning_achieved(
tune_results=tune_analysis_self_play,
min_=50)
check_learning_achieved(
tune_results=tune_analysis_self_play,
min_=0.3,
metric="custom_metrics.pick_speed/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis_self_play,
min_=0.3,
metric="custom_metrics.pick_speed/player_blue_mean")
check_learning_achieved(
tune_results=tune_analysis_self_play,
min_=0.9,
metric="custom_metrics.pick_own_color/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis_self_play,
min_=0.9,
metric="custom_metrics.pick_own_color/player_blue_mean")
print_metrics_available(tune_analysis_against_opponent)
check_learning_achieved(
tune_results=tune_analysis_against_opponent,
max_=20)
check_learning_achieved(
tune_results=tune_analysis_against_opponent,
min_=0.3,
metric="custom_metrics.pick_speed/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis_against_opponent,
min_=0.3,
metric="custom_metrics.pick_speed/player_blue_mean")
check_learning_achieved(
tune_results=tune_analysis_against_opponent,
max_=0.6,
metric="custom_metrics.pick_own_color/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis_against_opponent,
max_=0.6,
metric="custom_metrics.pick_own_color/player_blue_mean")
def test_amtft_ipd():
from marltoolbox.experiments.rllib_api.amtft_various_env import main
ray.shutdown()
tune_analysis_per_welfare, analysis_metrics_per_mode = main(
debug=False, train_n_replicates=1, filter_utilitarian=False,
env="IteratedPrisonersDilemma")
for welfare_name, tune_analysis in tune_analysis_per_welfare.items():
print("welfare_name", welfare_name)
print_metrics_available(tune_analysis)
check_learning_achieved(
tune_results=tune_analysis, min_=-204)
check_learning_achieved(
tune_results=tune_analysis,
min_=0.9,
metric="custom_metrics.CC_freq/player_row_mean"
)
check_learning_achieved(
tune_results=tune_analysis,
min_=0.9,
metric="custom_metrics.CC_freq/player_col_mean"
)
def test_dqn_wt_utilitarian_welfare_coin_game():
from marltoolbox.examples.rllib_api.dqn_wt_welfare import main
ray.shutdown()
tune_analysis = main(debug=False)
print_metrics_available(tune_analysis)
check_learning_achieved(
tune_results=tune_analysis, min_=50)
check_learning_achieved(
tune_results=tune_analysis,
min_=0.3,
metric="custom_metrics.pick_speed/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.3,
metric="custom_metrics.pick_speed/player_blue_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.95,
metric="custom_metrics.pick_own_color/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.95,
metric="custom_metrics.pick_own_color/player_blue_mean")
def test_dqn_wt_inequity_aversion_welfare_coin_game():
from marltoolbox.examples.rllib_api.dqn_wt_welfare import main
ray.shutdown()
tune_analysis = main(debug=False,
welfare=postprocessing.WELFARE_INEQUITY_AVERSION)
print_metrics_available(tune_analysis)
check_learning_achieved(
tune_results=tune_analysis, min_=50)
check_learning_achieved(
tune_results=tune_analysis,
min_=0.25,
metric="custom_metrics.pick_speed/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.25,
metric="custom_metrics.pick_speed/player_blue_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.9,
metric="custom_metrics.pick_own_color/player_red_mean")
check_learning_achieved(
tune_results=tune_analysis,
min_=0.9,
metric="custom_metrics.pick_own_color/player_blue_mean")
def test_adaptive_mechanism_design_tune_class_api_wt_rllib_policy():
from marltoolbox.examples.tune_class_api.amd import main
ray.shutdown(
) # Restart Ray defensively in case the ray connection is lost.
main(debug=True, use_rllib_policy=True)
def test_l1br_lola_pg_tune_class_api():
from marltoolbox.examples.tune_class_api.l1br_lola_pg import main
ray.shutdown(
) # Restart Ray defensively in case the ray connection is lost.
main(debug=True)
def test_lola_pg_tune_fn_api():
from marltoolbox.examples.tune_function_api.lola_pg_official import main
ray.shutdown(
) # Restart Ray defensively in case the ray connection is lost.
main(debug=True)
def test_l1br_amtft():
from marltoolbox.examples.rllib_api.l1br_amtft import main
ray.shutdown(
) # Restart Ray defensively in case the ray connection is lost.
main(debug=True)
def test_inequity_aversion():
from marltoolbox.examples.rllib_api.inequity_aversion import main
ray.shutdown(
) # Restart Ray defensively in case the ray connection is lost.
main(debug=True)
def test_inequity_aversion():
from marltoolbox.examples.rllib_api.inequity_aversion import main
ray.shutdown()
main(debug=True)
def test_amtft_asym_coin_game():
from marltoolbox.experiments.rllib_api.amtft_various_env import main
ray.shutdown()
main(debug=True, env="AsymCoinGame")
def test_ltft_ipd():
from marltoolbox.examples.rllib_api.ltft_ipd import main
ray.shutdown() # Restart Ray defensively in case the ray connection is lost.
tune_analysis_self_play, tune_analysis_naive_opponent = main(debug=False)
check_learning_achieved(tune_results=tune_analysis_self_play, reward=-42, min=True)
check_learning_achieved(tune_results=tune_analysis_naive_opponent, reward=-78, max=True)
def test_ppo_asym_coin_game():
from marltoolbox.examples.rllib_api.ppo_asymmetric_coin_game import main
ray.shutdown() # Restart Ray defensively in case the ray connection is lost.
tune_analysis = main(debug=False, stop_iters=70)
check_learning_achieved(tune_results=tune_analysis, reward=20, min=True)
def test_pg_ipd():
from marltoolbox.examples.rllib_api.pg_ipd import main
ray.shutdown() # Restart Ray defensively in case the ray connection is lost.
tune_analysis = main(debug=False)
check_learning_achieved(tune_results=tune_analysis, reward=-75, max=True)
def test_l1br_amtft_coin_game():
from marltoolbox.experiments.rllib_api.l1br_amtft import main
ray.shutdown()
main(debug=True, env="CoinGame")
def test_l1br_amtft_iasymbos():
from marltoolbox.experiments.rllib_api.l1br_amtft import main
ray.shutdown()
main(debug=True, env="IteratedAsymBoS")
def test_l1br_amtft_ipd():
from marltoolbox.experiments.rllib_api.l1br_amtft import main
ray.shutdown()
main(debug=True, env="IteratedPrisonersDilemma")
def test_ltft_coin_game():
from marltoolbox.experiments.rllib_api.ltft_various_env import main
ray.shutdown()
main(debug=True, env="CoinGame", train_n_replicates=1)
def test_ltft_ipd():
from marltoolbox.experiments.rllib_api.ltft_various_env import main
ray.shutdown()
main(debug=True, env="IteratedPrisonersDilemma", train_n_replicates=1)
def test_pg_ipd():
from marltoolbox.examples.rllib_api.pg_ipd import main
ray.shutdown(
) # Restart Ray defensively in case the ray connection is lost.
main(stop_iters=10, tf=False, debug=True)
def test_ppo_asym_coin_game():
from marltoolbox.examples.rllib_api.dqn_coin_game import main
ray.shutdown()
main(debug=True)
def test_amtft_mixed_motive_coin_game():
from marltoolbox.experiments.rllib_api.amtft_various_env import main
ray.shutdown()
main(debug=True, env="MixedMotiveCoinGame")
def test_amtft_ipd():
from marltoolbox.examples.rllib_api.amtft_various_env import main
ray.shutdown() # Restart Ray defensively in case the ray connection is lost.
tune_analysis_per_welfare, analysis_metrics_per_mode = main(debug=False, train_n_replicates=1, filter_utilitarian=False)
for welfare_name, tune_analysis in tune_analysis_per_welfare.items():
check_learning_achieved(tune_results=tune_analysis, reward=-204, min=True)
def test_adaptive_mechanism_design_tune_class_api_wt_rllib_policy():
from marltoolbox.experiments.tune_class_api.amd import main
ray.shutdown()
main(debug=True, use_rllib_policy=True)
def test_ppo_asym_coin_game():
from marltoolbox.examples.rllib_api.ppo_asymmetric_coin_game import main
ray.shutdown(
) # Restart Ray defensively in case the ray connection is lost.
main(stop_iters=3, tf=False, debug=True)
def test_l1br_lola_pg_tune_class_api():
from marltoolbox.experiments.tune_class_api.l1br_lola_pg import main
ray.shutdown()
main(debug=True)
