def test_recurrent_sac(use_discrete):
step_size = 0.5 if use_discrete else 0.2
env = MemoryEnvironment([BRAIN_NAME],
use_discrete=use_discrete,
step_size=step_size)
new_networksettings = attr.evolve(
SAC_TF_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16,
sequence_length=16),
)
new_hyperparams = attr.evolve(
SAC_TF_CONFIG.hyperparameters,
batch_size=128,
learning_rate=1e-3,
buffer_init_steps=1000,
steps_per_update=2,
)
config = attr.evolve(
SAC_TF_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_networksettings,
max_steps=5000,
framework=FrameworkType.TENSORFLOW,
)
_check_environment_trains(env, {BRAIN_NAME: config})
def test_recurrent_poca(action_sizes, is_multiagent):
if is_multiagent:
# This is not a recurrent environment, just check if LSTM doesn't crash
env = MultiAgentEnvironment([BRAIN_NAME],
action_sizes=action_sizes,
num_agents=2)
else:
# Actually test LSTM here
env = MemoryEnvironment([BRAIN_NAME], action_sizes=action_sizes)
new_network_settings = attr.evolve(
POCA_TORCH_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16),
)
new_hyperparams = attr.evolve(
POCA_TORCH_CONFIG.hyperparameters,
learning_rate=1.0e-3,
batch_size=64,
buffer_size=128,
)
config = attr.evolve(
POCA_TORCH_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_network_settings,
max_steps=500 if is_multiagent else 6000,
)
check_environment_trains(env, {BRAIN_NAME: config},
success_threshold=None if is_multiagent else 0.9)
def test_recurrent_ppo(use_discrete):
env = MemoryEnvironment([BRAIN_NAME], use_discrete=use_discrete)
override_vals = {
"max_steps": 5000,
"batch_size": 64,
"buffer_size": 128,
"learning_rate": 1e-3,
"use_recurrent": True,
}
config = generate_config(PPO_CONFIG, override_vals)
_check_environment_trains(env, config, success_threshold=0.9)
def test_recurrent_sac(use_discrete):
env = MemoryEnvironment([BRAIN_NAME], use_discrete=use_discrete)
override_vals = {
"batch_size": 64,
"use_recurrent": True,
"max_steps": 3000,
"learning_rate": 1e-3,
"buffer_init_steps": 500,
}
config = generate_config(SAC_CONFIG, override_vals)
_check_environment_trains(env, config)
def test_recurrent_ppo(use_discrete):
env = MemoryEnvironment([BRAIN_NAME], use_discrete=use_discrete)
new_network_settings = attr.evolve(
PPO_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16),
)
new_hyperparams = attr.evolve(
PPO_CONFIG.hyperparameters, learning_rate=1.0e-3, batch_size=64, buffer_size=128
)
config = attr.evolve(
PPO_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_network_settings,
max_steps=5000,
)
_check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
def test_hybrid_recurrent_ppo():
env = MemoryEnvironment([BRAIN_NAME], action_sizes=(1, 1), step_size=0.5)
new_network_settings = attr.evolve(
PPO_TORCH_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16),
)
new_hyperparams = attr.evolve(
PPO_TORCH_CONFIG.hyperparameters,
learning_rate=1.0e-3,
batch_size=64,
buffer_size=512,
)
config = attr.evolve(
PPO_TORCH_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_network_settings,
max_steps=3000,
)
check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
def test_hybrid_recurrent_sac():
env = MemoryEnvironment([BRAIN_NAME], action_sizes=(1, 1), step_size=0.5)
new_networksettings = attr.evolve(
SAC_TORCH_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16, sequence_length=16),
)
new_hyperparams = attr.evolve(
SAC_TORCH_CONFIG.hyperparameters,
batch_size=256,
learning_rate=1e-3,
buffer_init_steps=1000,
steps_per_update=2,
)
config = attr.evolve(
SAC_TORCH_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_networksettings,
max_steps=4000,
)
check_environment_trains(env, {BRAIN_NAME: config})
def test_recurrent_ppo(action_sizes):
env = MemoryEnvironment([BRAIN_NAME], action_sizes=action_sizes)
new_network_settings = attr.evolve(
PPO_TF_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16),
)
new_hyperparams = attr.evolve(
PPO_TF_CONFIG.hyperparameters,
learning_rate=1.0e-3,
batch_size=64,
buffer_size=128,
)
config = attr.evolve(
PPO_TF_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_network_settings,
max_steps=5000,
framework=FrameworkType.TENSORFLOW,
)
_check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
def test_recurrent_sac(use_discrete):
env = MemoryEnvironment([BRAIN_NAME], use_discrete=use_discrete)
new_networksettings = attr.evolve(
SAC_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16,
sequence_length=32),
)
new_hyperparams = attr.evolve(
SAC_CONFIG.hyperparameters,
batch_size=64,
learning_rate=1e-3,
buffer_init_steps=500,
steps_per_update=2,
)
config = attr.evolve(
SAC_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_networksettings,
max_steps=5000,
)
_check_environment_trains(env, {BRAIN_NAME: config})
def test_recurrent_sac(action_sizes):
step_size = 0.2 if action_sizes == (0, 1) else 0.5
env = MemoryEnvironment(
[BRAIN_NAME], action_sizes=action_sizes, step_size=step_size
)
new_networksettings = attr.evolve(
SAC_TORCH_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16, sequence_length=16),
)
new_hyperparams = attr.evolve(
SAC_TORCH_CONFIG.hyperparameters,
batch_size=256,
learning_rate=3e-4,
buffer_init_steps=1000,
steps_per_update=2,
)
config = attr.evolve(
SAC_TORCH_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_networksettings,
max_steps=4000,
)
check_environment_trains(env, {BRAIN_NAME: config}, training_seed=1337)
def test_recurrent_sac(action_sizes):
step_size = 0.2 if action_sizes == (0, 1) else 0.5
env = MemoryEnvironment([BRAIN_NAME],
action_sizes=action_sizes,
step_size=step_size)
new_networksettings = attr.evolve(
SAC_TF_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16),
)
new_hyperparams = attr.evolve(
SAC_TF_CONFIG.hyperparameters,
batch_size=128,
learning_rate=1e-3,
buffer_init_steps=1000,
steps_per_update=2,
)
config = attr.evolve(
SAC_TF_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_networksettings,
max_steps=4000,
framework=FrameworkType.TENSORFLOW,
)
_check_environment_trains(env, {BRAIN_NAME: config})
