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_visual_poca(num_visual):
env = MultiAgentEnvironment([BRAIN_NAME],
action_sizes=(0, 1),
num_agents=2,
num_visual=num_visual)
new_hyperparams = attr.evolve(POCA_TORCH_CONFIG.hyperparameters,
learning_rate=3.0e-4)
config = attr.evolve(POCA_TORCH_CONFIG, hyperparameters=new_hyperparams)
check_environment_trains(env, {BRAIN_NAME: config})
def test_var_len_obs_and_goal_poca(num_vis, num_vector, num_var_len,
conditioning_type):
env = MultiAgentEnvironment(
[BRAIN_NAME],
action_sizes=(0, 1),
num_visual=num_vis,
num_vector=num_vector,
num_var_len=num_var_len,
step_size=0.2,
num_agents=2,
goal_indices=[0],
)
new_network = attr.evolve(POCA_TORCH_CONFIG.network_settings,
goal_conditioning_type=conditioning_type)
new_hyperparams = attr.evolve(POCA_TORCH_CONFIG.hyperparameters,
learning_rate=3.0e-4)
config = attr.evolve(POCA_TORCH_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_network)
check_environment_trains(env, {BRAIN_NAME: config})
def test_simple_poca(action_sizes):
env = MultiAgentEnvironment([BRAIN_NAME],
action_sizes=action_sizes,
num_agents=2)
config = attr.evolve(POCA_TORCH_CONFIG)
check_environment_trains(env, {BRAIN_NAME: config})