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fixing errors

/develop/actionmodel-csharp
Andrew Cohen 4 年前
当前提交
db37db34
共有 7 个文件被更改,包括 401 次插入386 次删除
  1. 2
      ml-agents-envs/mlagents_envs/base_env.py
  2. 16
      ml-agents/mlagents/trainers/policy/policy.py
  3. 6
      ml-agents/mlagents/trainers/policy/torch_policy.py
  4. 9
      ml-agents/mlagents/trainers/tests/simple_test_envs.py
  5. 691
      ml-agents/mlagents/trainers/tests/torch/test_simple_rl.py
  6. 6
      ml-agents/mlagents/trainers/torch/distributions.py
  7. 57
      ml-agents/mlagents/trainers/torch/networks.py

2
ml-agents-envs/mlagents_envs/base_env.py


HYBRID = 2
class BehaviorSpec(NamedTuple):
class HybridBehaviorSpec(NamedTuple):
observation_shapes: List[Tuple]
continuous_action_shape: int
discrete_action_shape: Tuple[int]

16
ml-agents/mlagents/trainers/policy/policy.py


self.network_settings: NetworkSettings = trainer_settings.network_settings
self.seed = seed
# For hybrid
self.continuous_act_size = behavior_spec.continuous_action_size()
self.discrete_act_size = behavior_spec.continuous_action_size()
self.act_size = (
list(behavior_spec.discrete_action_branches)
if behavior_spec.is_action_discrete()
else [behavior_spec.action_size]
)
self.continuous_act_size = behavior_spec.continuous_action_size
self.discrete_act_size = behavior_spec.discrete_action_branches
#self.act_size = (
# list(behavior_spec.discrete_action_branches)
# if behavior_spec.is_action_discrete()
# else [behavior_spec.action_size]
#)
self.vec_obs_size = sum(
shape[0] for shape in behavior_spec.observation_shapes if len(shape) == 1
)

self.use_continuous_act = behavior_spec.is_action_continuous()
#self.use_continuous_act = behavior_spec.is_action_continuous()
self.num_branches = self.behavior_spec.action_size
self.previous_action_dict: Dict[str, np.array] = {}
self.memory_dict: Dict[str, np.ndarray] = {}

6
ml-agents/mlagents/trainers/policy/torch_policy.py


from mlagents.trainers.settings import TrainerSettings
from mlagents.trainers.trajectory import SplitObservations
from mlagents.trainers.torch.distributions import DistInstance
from mlagents.trainers.torch.networks import (
SharedActorCritic,
SeparateActorCritic,

self.actor_critic = ac_class(
observation_shapes=self.behavior_spec.observation_shapes,
network_settings=trainer_settings.network_settings,
act_type=behavior_spec.action_type,
continuous_act_size=self.continuous_act_size,
discrete_act_size=self.discrete_act_size,
stream_names=reward_signal_names,

if self.use_vec_obs and self.normalize:
self.actor_critic.update_normalization(vector_obs)
def get_actions_and_stats(dists : List[DistInstance]):
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
def get_actions_and_stats(dists : List[DistInstance]) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
action_list = self.actor_critic.sample_action(dists)
log_probs, entropies, all_logs = ModelUtils.get_probs_and_entropy(
action_list, dists

9
ml-agents/mlagents/trainers/tests/simple_test_envs.py


from mlagents_envs.base_env import (
BaseEnv,
BehaviorSpec,
DecisionSteps,
DecisionSteps,
TerminalSteps,
ActionType,
BehaviorMapping,

vis_obs_size,
vec_obs_size,
action_size,
)
super().__init__(
brain_names,
False,
step_size=step_size,
num_visual=num_visual,
num_vector=num_vector,
)
# Number of steps to reveal the goal for. Lower is harder. Should be
# less than 1/step_size to force agent to use memory

691
ml-agents/mlagents/trainers/tests/torch/test_simple_rl.py


from mlagents.trainers.tests.simple_test_envs import (
SimpleEnvironment,
HybridEnvironment,
MemoryEnvironment,
RecordEnvironment,
)

assert all(reward > success_threshold for reward in processed_rewards)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_ppo(use_discrete):
env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete)
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_simple_ppo(use_discrete):
# env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete)
# config = attr.evolve(PPO_CONFIG)
# _check_environment_trains(env, {BRAIN_NAME: config})
#
def test_hybrid_ppo():
env = HybridEnvironment([BRAIN_NAME])
@pytest.mark.parametrize("use_discrete", [True, False])
def test_2d_ppo(use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME], use_discrete=use_discrete, action_size=2, step_size=0.8
)
new_hyperparams = attr.evolve(
PPO_CONFIG.hyperparameters, batch_size=64, buffer_size=640
)
config = attr.evolve(PPO_CONFIG, hyperparameters=new_hyperparams, max_steps=10000)
_check_environment_trains(env, {BRAIN_NAME: config})
@pytest.mark.parametrize("use_discrete", [True, False])
@pytest.mark.parametrize("num_visual", [1, 2])
def test_visual_ppo(num_visual, use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME],
use_discrete=use_discrete,
num_visual=num_visual,
num_vector=0,
step_size=0.2,
)
new_hyperparams = attr.evolve(PPO_CONFIG.hyperparameters, learning_rate=3.0e-4)
config = attr.evolve(PPO_CONFIG, hyperparameters=new_hyperparams)
_check_environment_trains(env, {BRAIN_NAME: config})
@pytest.mark.parametrize("num_visual", [1, 2])
@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
def test_visual_advanced_ppo(vis_encode_type, num_visual):
env = SimpleEnvironment(
[BRAIN_NAME],
use_discrete=True,
num_visual=num_visual,
num_vector=0,
step_size=0.5,
vis_obs_size=(5, 5, 5) if vis_encode_type == "match3" else (36, 36, 3),
)
new_networksettings = attr.evolve(
SAC_CONFIG.network_settings, vis_encode_type=EncoderType(vis_encode_type)
)
new_hyperparams = attr.evolve(PPO_CONFIG.hyperparameters, learning_rate=3.0e-4)
config = attr.evolve(
PPO_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_networksettings,
max_steps=700,
summary_freq=100,
)
# The number of steps is pretty small for these encoders
_check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.5)
@pytest.mark.parametrize("use_discrete", [True, False])
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)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_sac(use_discrete):
env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete)
config = attr.evolve(SAC_CONFIG)
_check_environment_trains(env, {BRAIN_NAME: config})
@pytest.mark.parametrize("use_discrete", [True, False])
def test_2d_sac(use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME], use_discrete=use_discrete, action_size=2, step_size=0.8
)
new_hyperparams = attr.evolve(SAC_CONFIG.hyperparameters, buffer_init_steps=2000)
config = attr.evolve(SAC_CONFIG, hyperparameters=new_hyperparams, max_steps=10000)
_check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.8)
@pytest.mark.parametrize("use_discrete", [True, False])
@pytest.mark.parametrize("num_visual", [1, 2])
def test_visual_sac(num_visual, use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME],
use_discrete=use_discrete,
num_visual=num_visual,
num_vector=0,
step_size=0.2,
)
new_hyperparams = attr.evolve(
SAC_CONFIG.hyperparameters, batch_size=16, learning_rate=3e-4
)
config = attr.evolve(SAC_CONFIG, hyperparameters=new_hyperparams)
_check_environment_trains(env, {BRAIN_NAME: config})
@pytest.mark.parametrize("num_visual", [1, 2])
@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
def test_visual_advanced_sac(vis_encode_type, num_visual):
env = SimpleEnvironment(
[BRAIN_NAME],
use_discrete=True,
num_visual=num_visual,
num_vector=0,
step_size=0.5,
vis_obs_size=(5, 5, 5) if vis_encode_type == "match3" else (36, 36, 3),
)
new_networksettings = attr.evolve(
SAC_CONFIG.network_settings, vis_encode_type=EncoderType(vis_encode_type)
)
new_hyperparams = attr.evolve(
SAC_CONFIG.hyperparameters,
batch_size=16,
learning_rate=3e-4,
buffer_init_steps=0,
)
config = attr.evolve(
SAC_CONFIG,
hyperparameters=new_hyperparams,
network_settings=new_networksettings,
max_steps=100,
)
# The number of steps is pretty small for these encoders
_check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.5)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_recurrent_sac(use_discrete):
step_size = 0.2 if use_discrete else 0.5
env = MemoryEnvironment(
[BRAIN_NAME], use_discrete=use_discrete, step_size=step_size
)
new_networksettings = attr.evolve(
SAC_CONFIG.network_settings,
memory=NetworkSettings.MemorySettings(memory_size=16, sequence_length=16),
)
new_hyperparams = attr.evolve(
SAC_CONFIG.hyperparameters,
batch_size=128,
learning_rate=1e-3,
buffer_init_steps=1000,
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})
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_ghost(use_discrete):
env = SimpleEnvironment(
[BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
)
self_play_settings = SelfPlaySettings(
play_against_latest_model_ratio=1.0, save_steps=2000, swap_steps=2000
)
config = attr.evolve(PPO_CONFIG, self_play=self_play_settings, max_steps=2500)
_check_environment_trains(env, {BRAIN_NAME: config})
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_ghost_fails(use_discrete):
env = SimpleEnvironment(
[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.
self_play_settings = SelfPlaySettings(
play_against_latest_model_ratio=1.0, save_steps=2000, swap_steps=4000
)
config = attr.evolve(PPO_CONFIG, self_play=self_play_settings, max_steps=2500)
_check_environment_trains(env, {BRAIN_NAME: 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
)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_asymm_ghost(use_discrete):
# Make opponent for asymmetric case
brain_name_opp = BRAIN_NAME + "Opp"
env = SimpleEnvironment(
[BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], use_discrete=use_discrete
)
self_play_settings = SelfPlaySettings(
play_against_latest_model_ratio=1.0,
save_steps=10000,
swap_steps=10000,
team_change=400,
)
config = attr.evolve(PPO_CONFIG, self_play=self_play_settings, max_steps=4000)
_check_environment_trains(env, {BRAIN_NAME: config, brain_name_opp: config})
@pytest.mark.parametrize("use_discrete", [True, False])
def test_simple_asymm_ghost_fails(use_discrete):
# Make opponent for asymmetric case
brain_name_opp = BRAIN_NAME + "Opp"
env = SimpleEnvironment(
[BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], use_discrete=use_discrete
)
# This config should fail because the team that us not learning when both have reached
# max step should be executing the initial, untrained poliy.
self_play_settings = SelfPlaySettings(
play_against_latest_model_ratio=0.0,
save_steps=5000,
swap_steps=5000,
team_change=2000,
)
config = attr.evolve(PPO_CONFIG, self_play=self_play_settings, max_steps=3000)
_check_environment_trains(
env, {BRAIN_NAME: config, brain_name_opp: 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
)
@pytest.fixture(scope="session")
def simple_record(tmpdir_factory):
def record_demo(use_discrete, num_visual=0, num_vector=1):
env = RecordEnvironment(
[BRAIN_NAME],
use_discrete=use_discrete,
num_visual=num_visual,
num_vector=num_vector,
n_demos=100,
)
# If we want to use true demos, we can solve the env in the usual way
# Otherwise, we can just call solve to execute the optimal policy
env.solve()
agent_info_protos = env.demonstration_protos[BRAIN_NAME]
meta_data_proto = DemonstrationMetaProto()
brain_param_proto = BrainParametersProto(
vector_action_size=[2] if use_discrete else [1],
vector_action_descriptions=[""],
vector_action_space_type=discrete if use_discrete else continuous,
brain_name=BRAIN_NAME,
is_training=True,
)
action_type = "Discrete" if use_discrete else "Continuous"
demo_path_name = "1DTest" + action_type + ".demo"
demo_path = str(tmpdir_factory.mktemp("tmp_demo").join(demo_path_name))
write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos)
return demo_path
return record_demo
@pytest.mark.parametrize("use_discrete", [True, False])
@pytest.mark.parametrize("trainer_config", [PPO_CONFIG, SAC_CONFIG])
def test_gail(simple_record, use_discrete, trainer_config):
demo_path = simple_record(use_discrete)
env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete, step_size=0.2)
bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1000)
reward_signals = {
RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
}
config = attr.evolve(
trainer_config,
reward_signals=reward_signals,
behavioral_cloning=bc_settings,
max_steps=500,
)
_check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_gail_visual_ppo(simple_record, use_discrete):
demo_path = simple_record(use_discrete, num_visual=1, num_vector=0)
env = SimpleEnvironment(
[BRAIN_NAME],
num_visual=1,
num_vector=0,
use_discrete=use_discrete,
step_size=0.2,
)
bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1500)
reward_signals = {
RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
}
hyperparams = attr.evolve(PPO_CONFIG.hyperparameters, learning_rate=3e-4)
config = attr.evolve(
PPO_CONFIG,
reward_signals=reward_signals,
hyperparameters=hyperparams,
behavioral_cloning=bc_settings,
max_steps=1000,
)
_check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
@pytest.mark.parametrize("use_discrete", [True, False])
def test_gail_visual_sac(simple_record, use_discrete):
demo_path = simple_record(use_discrete, num_visual=1, num_vector=0)
env = SimpleEnvironment(
[BRAIN_NAME],
num_visual=1,
num_vector=0,
use_discrete=use_discrete,
step_size=0.2,
)
bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1000)
reward_signals = {
RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
}
hyperparams = attr.evolve(
SAC_CONFIG.hyperparameters, learning_rate=3e-4, batch_size=16
)
config = attr.evolve(
SAC_CONFIG,
reward_signals=reward_signals,
hyperparameters=hyperparams,
behavioral_cloning=bc_settings,
max_steps=500,
)
_check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_2d_ppo(use_discrete):
# env = SimpleEnvironment(
# [BRAIN_NAME], use_discrete=use_discrete, action_size=2, step_size=0.8
# )
# new_hyperparams = attr.evolve(
# PPO_CONFIG.hyperparameters, batch_size=64, buffer_size=640
# )
# config = attr.evolve(PPO_CONFIG, hyperparameters=new_hyperparams, max_steps=10000)
# _check_environment_trains(env, {BRAIN_NAME: config})
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#@pytest.mark.parametrize("num_visual", [1, 2])
#def test_visual_ppo(num_visual, use_discrete):
# env = SimpleEnvironment(
# [BRAIN_NAME],
# use_discrete=use_discrete,
# num_visual=num_visual,
# num_vector=0,
# step_size=0.2,
# )
# new_hyperparams = attr.evolve(PPO_CONFIG.hyperparameters, learning_rate=3.0e-4)
# config = attr.evolve(PPO_CONFIG, hyperparameters=new_hyperparams)
# _check_environment_trains(env, {BRAIN_NAME: config})
#
#
#@pytest.mark.parametrize("num_visual", [1, 2])
#@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
#def test_visual_advanced_ppo(vis_encode_type, num_visual):
# env = SimpleEnvironment(
# [BRAIN_NAME],
# use_discrete=True,
# num_visual=num_visual,
# num_vector=0,
# step_size=0.5,
# vis_obs_size=(5, 5, 5) if vis_encode_type == "match3" else (36, 36, 3),
# )
# new_networksettings = attr.evolve(
# SAC_CONFIG.network_settings, vis_encode_type=EncoderType(vis_encode_type)
# )
# new_hyperparams = attr.evolve(PPO_CONFIG.hyperparameters, learning_rate=3.0e-4)
# config = attr.evolve(
# PPO_CONFIG,
# hyperparameters=new_hyperparams,
# network_settings=new_networksettings,
# max_steps=700,
# summary_freq=100,
# )
# # The number of steps is pretty small for these encoders
# _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.5)
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#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)
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_simple_sac(use_discrete):
# env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete)
# config = attr.evolve(SAC_CONFIG)
# _check_environment_trains(env, {BRAIN_NAME: config})
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_2d_sac(use_discrete):
# env = SimpleEnvironment(
# [BRAIN_NAME], use_discrete=use_discrete, action_size=2, step_size=0.8
# )
# new_hyperparams = attr.evolve(SAC_CONFIG.hyperparameters, buffer_init_steps=2000)
# config = attr.evolve(SAC_CONFIG, hyperparameters=new_hyperparams, max_steps=10000)
# _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.8)
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#@pytest.mark.parametrize("num_visual", [1, 2])
#def test_visual_sac(num_visual, use_discrete):
# env = SimpleEnvironment(
# [BRAIN_NAME],
# use_discrete=use_discrete,
# num_visual=num_visual,
# num_vector=0,
# step_size=0.2,
# )
# new_hyperparams = attr.evolve(
# SAC_CONFIG.hyperparameters, batch_size=16, learning_rate=3e-4
# )
# config = attr.evolve(SAC_CONFIG, hyperparameters=new_hyperparams)
# _check_environment_trains(env, {BRAIN_NAME: config})
#
#
#@pytest.mark.parametrize("num_visual", [1, 2])
#@pytest.mark.parametrize("vis_encode_type", ["resnet", "nature_cnn", "match3"])
#def test_visual_advanced_sac(vis_encode_type, num_visual):
# env = SimpleEnvironment(
# [BRAIN_NAME],
# use_discrete=True,
# num_visual=num_visual,
# num_vector=0,
# step_size=0.5,
# vis_obs_size=(5, 5, 5) if vis_encode_type == "match3" else (36, 36, 3),
# )
# new_networksettings = attr.evolve(
# SAC_CONFIG.network_settings, vis_encode_type=EncoderType(vis_encode_type)
# )
# new_hyperparams = attr.evolve(
# SAC_CONFIG.hyperparameters,
# batch_size=16,
# learning_rate=3e-4,
# buffer_init_steps=0,
# )
# config = attr.evolve(
# SAC_CONFIG,
# hyperparameters=new_hyperparams,
# network_settings=new_networksettings,
# max_steps=100,
# )
# # The number of steps is pretty small for these encoders
# _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.5)
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_recurrent_sac(use_discrete):
# step_size = 0.2 if use_discrete else 0.5
# env = MemoryEnvironment(
# [BRAIN_NAME], use_discrete=use_discrete, step_size=step_size
# )
# new_networksettings = attr.evolve(
# SAC_CONFIG.network_settings,
# memory=NetworkSettings.MemorySettings(memory_size=16, sequence_length=16),
# )
# new_hyperparams = attr.evolve(
# SAC_CONFIG.hyperparameters,
# batch_size=128,
# learning_rate=1e-3,
# buffer_init_steps=1000,
# 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})
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_simple_ghost(use_discrete):
# env = SimpleEnvironment(
# [BRAIN_NAME + "?team=0", BRAIN_NAME + "?team=1"], use_discrete=use_discrete
# )
# self_play_settings = SelfPlaySettings(
# play_against_latest_model_ratio=1.0, save_steps=2000, swap_steps=2000
# )
# config = attr.evolve(PPO_CONFIG, self_play=self_play_settings, max_steps=2500)
# _check_environment_trains(env, {BRAIN_NAME: config})
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_simple_ghost_fails(use_discrete):
# env = SimpleEnvironment(
# [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.
# self_play_settings = SelfPlaySettings(
# play_against_latest_model_ratio=1.0, save_steps=2000, swap_steps=4000
# )
# config = attr.evolve(PPO_CONFIG, self_play=self_play_settings, max_steps=2500)
# _check_environment_trains(env, {BRAIN_NAME: 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
# )
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_simple_asymm_ghost(use_discrete):
# # Make opponent for asymmetric case
# brain_name_opp = BRAIN_NAME + "Opp"
# env = SimpleEnvironment(
# [BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], use_discrete=use_discrete
# )
# self_play_settings = SelfPlaySettings(
# play_against_latest_model_ratio=1.0,
# save_steps=10000,
# swap_steps=10000,
# team_change=400,
# )
# config = attr.evolve(PPO_CONFIG, self_play=self_play_settings, max_steps=4000)
# _check_environment_trains(env, {BRAIN_NAME: config, brain_name_opp: config})
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_simple_asymm_ghost_fails(use_discrete):
# # Make opponent for asymmetric case
# brain_name_opp = BRAIN_NAME + "Opp"
# env = SimpleEnvironment(
# [BRAIN_NAME + "?team=0", brain_name_opp + "?team=1"], use_discrete=use_discrete
# )
# # This config should fail because the team that us not learning when both have reached
# # max step should be executing the initial, untrained poliy.
# self_play_settings = SelfPlaySettings(
# play_against_latest_model_ratio=0.0,
# save_steps=5000,
# swap_steps=5000,
# team_change=2000,
# )
# config = attr.evolve(PPO_CONFIG, self_play=self_play_settings, max_steps=3000)
# _check_environment_trains(
# env, {BRAIN_NAME: config, brain_name_opp: 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
# )
#
#
#@pytest.fixture(scope="session")
#def simple_record(tmpdir_factory):
# def record_demo(use_discrete, num_visual=0, num_vector=1):
# env = RecordEnvironment(
# [BRAIN_NAME],
# use_discrete=use_discrete,
# num_visual=num_visual,
# num_vector=num_vector,
# n_demos=100,
# )
# # If we want to use true demos, we can solve the env in the usual way
# # Otherwise, we can just call solve to execute the optimal policy
# env.solve()
# agent_info_protos = env.demonstration_protos[BRAIN_NAME]
# meta_data_proto = DemonstrationMetaProto()
# brain_param_proto = BrainParametersProto(
# vector_action_size=[2] if use_discrete else [1],
# vector_action_descriptions=[""],
# vector_action_space_type=discrete if use_discrete else continuous,
# brain_name=BRAIN_NAME,
# is_training=True,
# )
# action_type = "Discrete" if use_discrete else "Continuous"
# demo_path_name = "1DTest" + action_type + ".demo"
# demo_path = str(tmpdir_factory.mktemp("tmp_demo").join(demo_path_name))
# write_demo(demo_path, meta_data_proto, brain_param_proto, agent_info_protos)
# return demo_path
#
# return record_demo
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#@pytest.mark.parametrize("trainer_config", [PPO_CONFIG, SAC_CONFIG])
#def test_gail(simple_record, use_discrete, trainer_config):
# demo_path = simple_record(use_discrete)
# env = SimpleEnvironment([BRAIN_NAME], use_discrete=use_discrete, step_size=0.2)
# bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1000)
# reward_signals = {
# RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
# }
# config = attr.evolve(
# trainer_config,
# reward_signals=reward_signals,
# behavioral_cloning=bc_settings,
# max_steps=500,
# )
# _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_gail_visual_ppo(simple_record, use_discrete):
# demo_path = simple_record(use_discrete, num_visual=1, num_vector=0)
# env = SimpleEnvironment(
# [BRAIN_NAME],
# num_visual=1,
# num_vector=0,
# use_discrete=use_discrete,
# step_size=0.2,
# )
# bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1500)
# reward_signals = {
# RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
# }
# hyperparams = attr.evolve(PPO_CONFIG.hyperparameters, learning_rate=3e-4)
# config = attr.evolve(
# PPO_CONFIG,
# reward_signals=reward_signals,
# hyperparameters=hyperparams,
# behavioral_cloning=bc_settings,
# max_steps=1000,
# )
# _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
#
#
#@pytest.mark.parametrize("use_discrete", [True, False])
#def test_gail_visual_sac(simple_record, use_discrete):
# demo_path = simple_record(use_discrete, num_visual=1, num_vector=0)
# env = SimpleEnvironment(
# [BRAIN_NAME],
# num_visual=1,
# num_vector=0,
# use_discrete=use_discrete,
# step_size=0.2,
# )
# bc_settings = BehavioralCloningSettings(demo_path=demo_path, steps=1000)
# reward_signals = {
# RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
# }
# hyperparams = attr.evolve(
# SAC_CONFIG.hyperparameters, learning_rate=3e-4, batch_size=16
# )
# config = attr.evolve(
# SAC_CONFIG,
# reward_signals=reward_signals,
# hyperparameters=hyperparams,
# behavioral_cloning=bc_settings,
# max_steps=500,
# )
# _check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)

6
ml-agents/mlagents/trainers/torch/distributions.py


self,
hidden_size: int,
continuous_act_size: int,
discrete_act_size: int,
discrete_act_size: List[int],
self.encoding_size = hidden_size
self.continuous_distributions: List[GaussianDistribution] = []
self.discrete_distributions: List[MultiCategoricalDistribution] = []
if continuous_act_size > 0:

tanh_squash=tanh_squash,
)
)
if discrete_act_size > 0:
if len(discrete_act_size) > 0:
self.discrete_distributions.append(
MultiCategoricalDistribution(self.encoding_size, discrete_act_size)
)

57
ml-agents/mlagents/trainers/torch/networks.py


self.output_distributions = OutputDistributions(
self.encoding_size,
continuous_act_size[0],
continuous_act_size,
discrete_act_size,
conditional_sigma=conditional_sigma,
tanh_squash=tanh_squash,

tanh_squash: bool = False,
):
super().__init__(
self,
act_type,
act_size,
continuous_act_size,
discrete_act_size,
conditional_sigma,
tanh_squash,
)

tanh_squash: bool = False,
):
super().__init__(
self,
act_type,
act_size,
continuous_act_size,
discrete_act_size,
print("CREATED", self.memory_size)
@property
def memory_size(self) -> int:

# self.critic.network_body.update_normalization(vector_obs)
#
#
# class GlobalSteps(nn.Module):
# def __init__(self):
# super().__init__()
# self.__global_step = nn.Parameter(torch.Tensor([0]), requires_grad=False)
#
# @property
# def current_step(self):
# return int(self.__global_step.item())
#
# @current_step.setter
# def current_step(self, value):
# self.__global_step[:] = value
#
# def increment(self, value):
# self.__global_step += value
#
#
# class LearningRate(nn.Module):
# def __init__(self, lr):
# # Todo: add learning rate decay
# super().__init__()
# self.learning_rate = torch.Tensor([lr])
class GlobalSteps(nn.Module):
def __init__(self):
super().__init__()
self.__global_step = nn.Parameter(torch.Tensor([0]), requires_grad=False)
@property
def current_step(self):
return int(self.__global_step.item())
@current_step.setter
def current_step(self, value):
self.__global_step[:] = value
def increment(self, value):
self.__global_step += value
class LearningRate(nn.Module):
def __init__(self, lr):
# Todo: add learning rate decay
super().__init__()
self.learning_rate = torch.Tensor([lr])
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