Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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import pytest
import numpy as np
from mlagents_envs.base_env import (
DecisionSteps,
TerminalSteps,
ActionSpec,
BehaviorSpec,
)
from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
def test_decision_steps():
ds = DecisionSteps(
obs=[np.array(range(12), dtype=np.float32).reshape(3, 4)],
reward=np.array(range(3), dtype=np.float32),
agent_id=np.array(range(10, 13), dtype=np.int32),
action_mask=[np.zeros((3, 4), dtype=np.bool)],
)
assert ds.agent_id_to_index[10] == 0
assert ds.agent_id_to_index[11] == 1
assert ds.agent_id_to_index[12] == 2
with pytest.raises(KeyError):
assert ds.agent_id_to_index[-1] == -1
mask_agent = ds[10].action_mask
assert isinstance(mask_agent, list)
assert len(mask_agent) == 1
assert np.array_equal(mask_agent[0], np.zeros((4), dtype=np.bool))
for agent_id in ds:
assert ds.agent_id_to_index[agent_id] in range(3)
def test_empty_decision_steps():
specs = BehaviorSpec(
observation_specs=create_observation_specs_with_shapes([(3, 2), (5,)]),
action_spec=ActionSpec.create_continuous(3),
)
ds = DecisionSteps.empty(specs)
assert len(ds.obs) == 2
assert ds.obs[0].shape == (0, 3, 2)
assert ds.obs[1].shape == (0, 5)
def test_terminal_steps():
ts = TerminalSteps(
obs=[np.array(range(12), dtype=np.float32).reshape(3, 4)],
reward=np.array(range(3), dtype=np.float32),
agent_id=np.array(range(10, 13), dtype=np.int32),
interrupted=np.array([1, 0, 1], dtype=np.bool),
)
assert ts.agent_id_to_index[10] == 0
assert ts.agent_id_to_index[11] == 1
assert ts.agent_id_to_index[12] == 2
assert ts[10].interrupted
assert not ts[11].interrupted
assert ts[12].interrupted
with pytest.raises(KeyError):
assert ts.agent_id_to_index[-1] == -1
for agent_id in ts:
assert ts.agent_id_to_index[agent_id] in range(3)
def test_empty_terminal_steps():
specs = BehaviorSpec(
observation_specs=create_observation_specs_with_shapes([(3, 2), (5,)]),
action_spec=ActionSpec.create_continuous(3),
)
ts = TerminalSteps.empty(specs)
assert len(ts.obs) == 2
assert ts.obs[0].shape == (0, 3, 2)
assert ts.obs[1].shape == (0, 5)
def test_specs():
specs = ActionSpec.create_continuous(3)
assert specs.discrete_branches == ()
assert specs.discrete_size == 0
assert specs.continuous_size == 3
assert specs.empty_action(5).continuous.shape == (5, 3)
assert specs.empty_action(5).continuous.dtype == np.float32
specs = ActionSpec.create_discrete((3,))
assert specs.discrete_branches == (3,)
assert specs.discrete_size == 1
assert specs.continuous_size == 0
assert specs.empty_action(5).discrete.shape == (5, 1)
assert specs.empty_action(5).discrete.dtype == np.int32
specs = ActionSpec(3, (3,))
assert specs.continuous_size == 3
assert specs.discrete_branches == (3,)
assert specs.discrete_size == 1
assert specs.empty_action(5).continuous.shape == (5, 3)
assert specs.empty_action(5).continuous.dtype == np.float32
assert specs.empty_action(5).discrete.shape == (5, 1)
assert specs.empty_action(5).discrete.dtype == np.int32
def test_action_generator():
# Continuous
action_len = 30
specs = ActionSpec.create_continuous(action_len)
zero_action = specs.empty_action(4).continuous
assert np.array_equal(zero_action, np.zeros((4, action_len), dtype=np.float32))
print(specs.random_action(4))
random_action = specs.random_action(4).continuous
print(random_action)
assert random_action.dtype == np.float32
assert random_action.shape == (4, action_len)
assert np.min(random_action) >= -1
assert np.max(random_action) <= 1
# Discrete
action_shape = (10, 20, 30)
specs = ActionSpec.create_discrete(action_shape)
zero_action = specs.empty_action(4).discrete
assert np.array_equal(zero_action, np.zeros((4, len(action_shape)), dtype=np.int32))
random_action = specs.random_action(4).discrete
assert random_action.dtype == np.int32
assert random_action.shape == (4, len(action_shape))
assert np.min(random_action) >= 0
for index, branch_size in enumerate(action_shape):
assert np.max(random_action[:, index]) < branch_size