您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
63 行
2.3 KiB
63 行
2.3 KiB
import numpy as np
|
|
from mlagents.torch_utils import torch
|
|
|
|
from mlagents.trainers.buffer import AgentBuffer, BufferKey
|
|
from mlagents.trainers.torch.agent_action import AgentAction
|
|
|
|
|
|
def test_agent_action_group_from_buffer():
|
|
buff = AgentBuffer()
|
|
# Create some actions
|
|
for _ in range(3):
|
|
buff[BufferKey.GROUP_CONTINUOUS_ACTION].append(
|
|
3 * [np.ones((5,), dtype=np.float32)]
|
|
)
|
|
buff[BufferKey.GROUP_DISCRETE_ACTION].append(
|
|
3 * [np.ones((4,), dtype=np.float32)]
|
|
)
|
|
# Some agents have died
|
|
for _ in range(2):
|
|
buff[BufferKey.GROUP_CONTINUOUS_ACTION].append(
|
|
1 * [np.ones((5,), dtype=np.float32)]
|
|
)
|
|
buff[BufferKey.GROUP_DISCRETE_ACTION].append(
|
|
1 * [np.ones((4,), dtype=np.float32)]
|
|
)
|
|
|
|
# Get the group actions, which will be a List of Lists of AgentAction, where each element is the same
|
|
# length as the AgentBuffer but contains only one agent's obs. Dead agents are padded by
|
|
# NaNs.
|
|
gact = AgentAction.group_from_buffer(buff)
|
|
# Agent 0 is full
|
|
agent_0_act = gact[0]
|
|
assert agent_0_act.continuous_tensor.shape == (buff.num_experiences, 5)
|
|
assert agent_0_act.discrete_tensor.shape == (buff.num_experiences, 4)
|
|
|
|
agent_1_act = gact[1]
|
|
assert agent_1_act.continuous_tensor.shape == (buff.num_experiences, 5)
|
|
assert agent_1_act.discrete_tensor.shape == (buff.num_experiences, 4)
|
|
assert (agent_1_act.continuous_tensor[0:3] > 0).all()
|
|
assert (agent_1_act.continuous_tensor[3:] == 0).all()
|
|
assert (agent_1_act.discrete_tensor[0:3] > 0).all()
|
|
assert (agent_1_act.discrete_tensor[3:] == 0).all()
|
|
|
|
|
|
def test_to_flat():
|
|
# Both continuous and discrete
|
|
aa = AgentAction(
|
|
torch.tensor([[1.0, 1.0, 1.0]]), [torch.tensor([2]), torch.tensor([1])]
|
|
)
|
|
flattened_actions = aa.to_flat([3, 3])
|
|
assert torch.eq(
|
|
flattened_actions, torch.tensor([[1, 1, 1, 0, 0, 1, 0, 1, 0]])
|
|
).all()
|
|
|
|
# Just continuous
|
|
aa = AgentAction(torch.tensor([[1.0, 1.0, 1.0]]), None)
|
|
flattened_actions = aa.to_flat([])
|
|
assert torch.eq(flattened_actions, torch.tensor([1, 1, 1])).all()
|
|
|
|
# Just discrete
|
|
aa = AgentAction(torch.tensor([]), [torch.tensor([2]), torch.tensor([1])])
|
|
flattened_actions = aa.to_flat([3, 3])
|
|
assert torch.eq(flattened_actions, torch.tensor([0, 0, 1, 0, 1, 0])).all()
|