Hugging Face's logo

AnnaMats
/
ppo-Pyramids-Training

Reinforcement Learning
ml-agents
TensorBoard
ONNX
Pyramids
deep-reinforcement-learning
ML-Agents-Pyramids
Model card Files Metrics Community
ppo-Pyramids-Training
File size: 6,612 Bytes 
05c9ac2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
 
from unittest.mock import patch

import pytest

from mlagents.trainers.agent_processor import AgentManagerQueue
from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
from mlagents.trainers.settings import RunOptions
from mlagents.trainers.tests import mock_brain as mb
from mlagents.trainers.tests.dummy_config import (
    create_observation_specs_with_shapes,
    ppo_dummy_config,
    poca_dummy_config,
    sac_dummy_config,
)
from mlagents.trainers.tests.mock_brain import make_fake_trajectory
from mlagents.trainers.trainer import TrainerFactory


@pytest.fixture
def ppo_config():
    return RunOptions(behaviors={"test_brain": ppo_dummy_config()})


@pytest.fixture
def sac_config():
    return RunOptions(behaviors={"test_brain": sac_dummy_config()})


@pytest.fixture
def poca_config():
    return RunOptions(behaviors={"test_brain": poca_dummy_config()})


def test_ppo_trainer_update_normalization(ppo_config):
    behavior_id_team0 = "test_brain?team=0"
    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
    mock_specs = mb.setup_test_behavior_specs(
        True, False, vector_action_space=[2], vector_obs_space=1
    )
    base_config = ppo_config.behaviors
    output_path = "results_dir"
    train_model = True
    load_model = False
    seed = 42
    trainer_factory = TrainerFactory(
        trainer_config=base_config,
        output_path=output_path,
        train_model=train_model,
        load_model=load_model,
        seed=seed,
        param_manager=EnvironmentParameterManager(),
    )
    ppo_trainer = trainer_factory.generate(brain_name)
    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
    policy = ppo_trainer.create_policy(parsed_behavior_id0, mock_specs)
    ppo_trainer.add_policy(parsed_behavior_id0, policy)
    trajectory_queue0 = AgentManagerQueue(behavior_id_team0)
    ppo_trainer.subscribe_trajectory_queue(trajectory_queue0)
    time_horizon = 15
    trajectory = make_fake_trajectory(
        length=time_horizon,
        max_step_complete=True,
        observation_specs=create_observation_specs_with_shapes([(1,)]),
        action_spec=mock_specs.action_spec,
    )
    trajectory_queue0.put(trajectory)
    # mocking out update_normalization in both the policy and critic
    with patch(
        "mlagents.trainers.torch_entities.networks.ValueNetwork.update_normalization"
    ) as optimizer_update_normalization_mock, patch(
        "mlagents.trainers.torch_entities.networks.SimpleActor.update_normalization"
    ) as policy_update_normalization_mock:
        ppo_trainer.advance()
        optimizer_update_normalization_mock.assert_called_once()
        policy_update_normalization_mock.assert_called_once()


def test_sac_trainer_update_normalization(sac_config):
    behavior_id_team0 = "test_brain?team=0"
    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
    mock_specs = mb.setup_test_behavior_specs(
        True, False, vector_action_space=[2], vector_obs_space=1
    )
    base_config = sac_config.behaviors
    output_path = "results_dir"
    train_model = True
    load_model = False
    seed = 42
    trainer_factory = TrainerFactory(
        trainer_config=base_config,
        output_path=output_path,
        train_model=train_model,
        load_model=load_model,
        seed=seed,
        param_manager=EnvironmentParameterManager(),
    )
    sac_trainer = trainer_factory.generate(brain_name)
    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
    policy = sac_trainer.create_policy(parsed_behavior_id0, mock_specs)
    sac_trainer.add_policy(parsed_behavior_id0, policy)
    trajectory_queue0 = AgentManagerQueue(behavior_id_team0)
    sac_trainer.subscribe_trajectory_queue(trajectory_queue0)
    time_horizon = 15
    trajectory = make_fake_trajectory(
        length=time_horizon,
        max_step_complete=True,
        observation_specs=create_observation_specs_with_shapes([(1,)]),
        action_spec=mock_specs.action_spec,
    )
    trajectory_queue0.put(trajectory)
    # mocking out update_normalization in both the policy and critic
    with patch(
        "mlagents.trainers.torch_entities.networks.ValueNetwork.update_normalization"
    ) as optimizer_update_normalization_mock, patch(
        "mlagents.trainers.torch_entities.networks.SimpleActor.update_normalization"
    ) as policy_update_normalization_mock:
        sac_trainer.advance()
        optimizer_update_normalization_mock.assert_called_once()
        policy_update_normalization_mock.assert_called_once()


def test_poca_trainer_update_normalization(poca_config):
    behavior_id_team0 = "test_brain?team=0"
    brain_name = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0).brain_name
    mock_specs = mb.setup_test_behavior_specs(
        True, False, vector_action_space=[2], vector_obs_space=1
    )
    base_config = poca_config.behaviors
    output_path = "results_dir"
    train_model = True
    load_model = False
    seed = 42
    trainer_factory = TrainerFactory(
        trainer_config=base_config,
        output_path=output_path,
        train_model=train_model,
        load_model=load_model,
        seed=seed,
        param_manager=EnvironmentParameterManager(),
    )
    poca_trainer = trainer_factory.generate(brain_name)
    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(behavior_id_team0)
    policy = poca_trainer.create_policy(parsed_behavior_id0, mock_specs)
    poca_trainer.add_policy(parsed_behavior_id0, policy)
    trajectory_queue0 = AgentManagerQueue(behavior_id_team0)
    poca_trainer.subscribe_trajectory_queue(trajectory_queue0)
    time_horizon = 15
    trajectory = make_fake_trajectory(
        length=time_horizon,
        max_step_complete=True,
        observation_specs=create_observation_specs_with_shapes([(1,)]),
        action_spec=mock_specs.action_spec,
    )
    trajectory_queue0.put(trajectory)
    # mocking out update_normalization in both the policy and critic
    with patch(
        "mlagents.trainers.poca.optimizer_torch.TorchPOCAOptimizer.POCAValueNetwork.update_normalization"
    ) as optimizer_update_normalization_mock, patch(
        "mlagents.trainers.torch_entities.networks.SimpleActor.update_normalization"
    ) as policy_update_normalization_mock:
        poca_trainer.advance()
        optimizer_update_normalization_mock.assert_called_once()
        policy_update_normalization_mock.assert_called_once()