def test_simple_metacurriculum(curriculum_brain_name):
env = Simple1DEnvironment([BRAIN_NAME], use_discrete=False)
curriculum_config = json.loads(dummy_curriculum_json_str)
mc = MetaCurriculum({curriculum_brain_name: curriculum_config})
_check_environment_trains(env,
TRAINER_CONFIG,
meta_curriculum=mc,
success_threshold=None)
def test_visual_sac(num_visual, use_discrete):
env = Simple1DEnvironment([BRAIN_NAME],
use_discrete=use_discrete,
num_visual=num_visual,
num_vector=0)
override_vals = {"batch_size": 16, "learning_rate": 3e-4}
config = generate_config(SAC_CONFIG, override_vals)
_check_environment_trains(env, config)
def test_visual_ppo(num_visual, use_discrete):
env = Simple1DEnvironment([BRAIN_NAME],
use_discrete=use_discrete,
num_visual=num_visual,
num_vector=0)
override_vals = {"learning_rate": 3.0e-4}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config)
def test_simple_ghost_fails(use_discrete):
env = Simple1DEnvironment(
[BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
)
_check_environment_trains(env, GHOST_CONFIG_FAIL, success_threshold=None)
processed_rewards = [
default_reward_processor(rewards) for rewards in env.final_rewards.values()
]
success_threshold = 0.99
assert any(reward > success_threshold for reward in processed_rewards) and any(
reward < success_threshold for reward in processed_rewards
)
def test_simple_ghost(use_discrete):
env = Simple1DEnvironment([BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"],
use_discrete=use_discrete)
override_vals = {
"max_steps": 2500,
"self_play": {
"play_against_current_self_ratio": 1.0,
"save_steps": 2000,
"swap_steps": 2000,
},
}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config)
def test_visual_advanced_ppo(vis_encode_type, num_visual):
env = Simple1DEnvironment(
[BRAIN_NAME],
use_discrete=True,
num_visual=num_visual,
num_vector=0,
step_size=0.5,
vis_obs_size=(36, 36, 3),
)
override_vals = {
"learning_rate": 3.0e-4,
"vis_encode_type": vis_encode_type,
"max_steps": 500,
"summary_freq": 100,
}
config = generate_config(PPO_CONFIG, override_vals)
# The number of steps is pretty small for these encoders
_check_environment_trains(env, config, success_threshold=0.5)
def test_simple_ghost_fails(use_discrete):
env = Simple1DEnvironment(
[BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
)
# This config should fail because the ghosted policy is never swapped with a competent policy.
# Swap occurs after max step is reached.
override_vals = {
"max_steps": 2500,
"self_play": {
"play_against_current_self_ratio": 1.0,
"save_steps": 2000,
"swap_steps": 4000,
},
}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config, success_threshold=None)
processed_rewards = [
default_reward_processor(rewards) for rewards in env.final_rewards.values()
]
success_threshold = 0.9
assert any(reward > success_threshold for reward in processed_rewards) and any(
reward < success_threshold for reward in processed_rewards
)
def test_simple_sac(use_discrete):
env = Simple1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
config = generate_config(SAC_CONFIG)
_check_environment_trains(env, config)
def test_simple_ghost(use_discrete):
env = Simple1DEnvironment(
[BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
)
_check_environment_trains(env, GHOST_CONFIG_PASS)
def test_simple_sac(use_discrete):
env = Simple1DEnvironment([BRAIN_NAME], use_discrete=use_discrete)
_check_environment_trains(env, SAC_CONFIG)
def simple_env_factory(worker_id, config):
env = Simple1DEnvironment(["1D"], use_discrete=True)
return env
