MarlGroupMapType

torchrl.envs.MarlGroupMapType(value, names=None, *, module=None, qualname=None, type=None, start=1)

Marl 组映射类型。

作为 torchrl 多智能体的功能，您可以控制环境中智能体的分组。您可以将智能体分组在一起（将它们的张量堆叠在一起），以在将它们通过相同的 神经网络时利用向量化。您可以将智能体分成不同的组，在这些组中它们是异构的或应该由不同的 神经网络处理。要进行分组，您只需要在环境构建时传递一个 group_map。

否则，您可以从该类中选择一种预制的分组策略。

• 使用 group_map=MarlGroupMapType.ALL_IN_ONE_GROUP 和智能体 ["agent_0", "agent_1", "agent_2", "agent_3"]，来自您的环境的进出张量字典将类似于

  >>> print(env.rand_action(env.reset()))
  TensorDict(
      fields={
          agents: TensorDict(
              fields={
                  action: Tensor(shape=torch.Size([4, 9]), device=cpu, dtype=torch.int64, is_shared=False),
                  done: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                  observation: Tensor(shape=torch.Size([4, 3, 3, 2]), device=cpu, dtype=torch.int8, is_shared=False)},
              batch_size=torch.Size([4]))},
      batch_size=torch.Size([]))
  >>> print(env.group_map)
  {"agents": ["agent_0", "agent_1", "agent_2", "agent_3]}
  

• 使用 group_map=MarlGroupMapType.ONE_GROUP_PER_AGENT 和智能体 ["agent_0", "agent_1", "agent_2", "agent_3"]，来自您的环境的进出张量字典将类似于

  >>> print(env.rand_action(env.reset()))
  TensorDict(
      fields={
          agent_0: TensorDict(
              fields={
                  action: Tensor(shape=torch.Size([9]), device=cpu, dtype=torch.int64, is_shared=False),
                  done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
                  observation: Tensor(shape=torch.Size([3, 3, 2]), device=cpu, dtype=torch.int8, is_shared=False)},
              batch_size=torch.Size([]))},
          agent_1: TensorDict(
              fields={
                  action: Tensor(shape=torch.Size([9]), device=cpu, dtype=torch.int64, is_shared=False),
                  done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
                  observation: Tensor(shape=torch.Size([3, 3, 2]), device=cpu, dtype=torch.int8, is_shared=False)},
              batch_size=torch.Size([]))},
          agent_2: TensorDict(
              fields={
                  action: Tensor(shape=torch.Size([9]), device=cpu, dtype=torch.int64, is_shared=False),
                  done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
                  observation: Tensor(shape=torch.Size([3, 3, 2]), device=cpu, dtype=torch.int8, is_shared=False)},
              batch_size=torch.Size([]))},
          agent_3: TensorDict(
              fields={
                  action: Tensor(shape=torch.Size([9]), device=cpu, dtype=torch.int64, is_shared=False),
                  done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
                  observation: Tensor(shape=torch.Size([3, 3, 2]), device=cpu, dtype=torch.int8, is_shared=False)},
              batch_size=torch.Size([]))},
      batch_size=torch.Size([]))
  >>> print(env.group_map)
  {"agent_0": ["agent_0"], "agent_1": ["agent_1"], "agent_2": ["agent_2"], "agent_3": ["agent_3"]}