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 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()