MarlGroupMapType

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

Marl 组映射类型。

作为 torchrl 多代理功能的一项特性，您可以控制环境中代理的分组。您可以将代理分组在一起（堆叠它们的张量）以在将它们通过同一神经网络时利用矢量化。您可以将代理分到不同的组，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，您只需要在环境构造时传递一个 `group_map`。

否则，您可以选择此类的预制分组策略之一。

• 使用 `group_map=MarlGroupMapType.ALL_IN_ONE_GROUP` 和代理 `["agent_0", "agent_1", "agent_2", "agent_3"]`，来自您的环境的传入和传出 tensordicts 将如下所示：

  >>> 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` 和代理 `["agent_0", "agent_1", "agent_2", "agent_3"]`，来自您的环境的传入和传出 tensordicts 将如下所示：

  >>> 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"]}