MarlGroupMapType

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

Marl Group Map Type.

As a feature of torchrl multiagent, you are able to control the grouping of agents in your environment. You can group agents together (stacking their tensors) to leverage vectorization when passing them through the same neural network. You can split agents in different groups where they are heterogenous or should be processed by different neural networks. To group, you just need to pass a group_map at env constructiuon time.

Otherwise, you can choose one of the premade grouping strategies from this class.

• With group_map=MarlGroupMapType.ALL_IN_ONE_GROUP and agents ["agent_0", "agent_1", "agent_2", "agent_3"], the tensordicts coming and going from your environment will look something like:

  >>> 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]}
  

• With group_map=MarlGroupMapType.ONE_GROUP_PER_AGENT and agents ["agent_0", "agent_1", "agent_2", "agent_3"], the tensordicts coming and going from your environment will look something like:

  >>> 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"]}