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52 行
1.9 KiB
52 行
1.9 KiB
import numpy as np
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from mlagents.torch_utils import torch
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from mlagents.trainers.buffer import AgentBuffer, BufferKey
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from mlagents.trainers.torch.agent_action import AgentAction
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def test_agent_action_group_from_buffer():
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buff = AgentBuffer()
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# Create some actions
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for _ in range(3):
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buff[BufferKey.GROUP_CONTINUOUS_ACTION].append(
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3 * [np.ones((5,), dtype=np.float32)]
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)
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buff[BufferKey.GROUP_DISCRETE_ACTION].append(
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3 * [np.ones((4,), dtype=np.float32)]
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)
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# Some agents have died
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for _ in range(2):
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buff[BufferKey.GROUP_CONTINUOUS_ACTION].append(
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1 * [np.ones((5,), dtype=np.float32)]
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)
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buff[BufferKey.GROUP_DISCRETE_ACTION].append(
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1 * [np.ones((4,), dtype=np.float32)]
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)
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# Get the group actions, which will be a List of Lists of AgentAction, where each element is the same
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# length as the AgentBuffer but contains only one agent's obs. Dead agents are padded by
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# NaNs.
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gact = AgentAction.group_from_buffer(buff)
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# Agent 0 is full
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agent_0_act = gact[0]
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assert agent_0_act.continuous_tensor.shape == (buff.num_experiences, 5)
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assert agent_0_act.discrete_tensor.shape == (buff.num_experiences, 4)
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agent_1_act = gact[1]
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assert agent_1_act.continuous_tensor.shape == (buff.num_experiences, 5)
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assert agent_1_act.discrete_tensor.shape == (buff.num_experiences, 4)
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assert (agent_1_act.continuous_tensor[0:3] > 0).all()
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assert (agent_1_act.continuous_tensor[3:] == 0).all()
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assert (agent_1_act.discrete_tensor[0:3] > 0).all()
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assert (agent_1_act.discrete_tensor[3:] == 0).all()
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def test_to_flat():
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aa = AgentAction(
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torch.tensor([[1.0, 1.0, 1.0]]), [torch.tensor([2]), torch.tensor([1])]
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)
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flattened_actions = aa.to_flat([3, 3])
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assert torch.eq(
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flattened_actions, torch.tensor([[1, 1, 1, 0, 0, 1, 0, 1, 0]])
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).all()
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