SMACv2Env

torchrl.envs.SMACv2Env(*args, **kwargs)

SMACv2（星际争霸多智能体挑战 v2）环境包装器。

要安装环境，请遵循以下 指南。

示例

>>> from torchrl.envs.libs.smacv2 import SMACv2Env
>>> print(SMACv2Env.available_envs)
['10gen_terran', '10gen_zerg', '10gen_protoss', '3m', '8m', '25m', '5m_vs_6m', '8m_vs_9m', '10m_vs_11m',
 '27m_vs_30m', 'MMM', 'MMM2', '2s3z', '3s5z', '3s5z_vs_3s6z', '3s_vs_3z', '3s_vs_4z', '3s_vs_5z', '1c3s5z',
  '2m_vs_1z', 'corridor', '6h_vs_8z', '2s_vs_1sc', 'so_many_baneling', 'bane_vs_bane', '2c_vs_64zg']
>>> # You can use old SMAC maps
>>> env = SMACv2Env(map_name="MMM2")
>>> print(env.rollout(5)
TensorDict(
    fields={
        agents: TensorDict(
            fields={
                action: Tensor(shape=torch.Size([5, 10, 18]), device=cpu, dtype=torch.int64, is_shared=False),
                action_mask: Tensor(shape=torch.Size([5, 10, 18]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([5, 10, 176]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([5, 10]),
            device=cpu,
            is_shared=False),
        done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        info: TensorDict(
            fields={
                battle_won: Tensor(shape=torch.Size([5]), device=cpu, dtype=torch.bool, is_shared=False),
                dead_allies: Tensor(shape=torch.Size([5]), device=cpu, dtype=torch.int64, is_shared=False),
                dead_enemies: Tensor(shape=torch.Size([5]), device=cpu, dtype=torch.int64, is_shared=False),
                episode_limit: Tensor(shape=torch.Size([5]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([5]),
            device=cpu,
            is_shared=False),
        next: TensorDict(
            fields={
                agents: TensorDict(
                    fields={
                        action_mask: Tensor(shape=torch.Size([5, 10, 18]), device=cpu, dtype=torch.bool, is_shared=False),
                        observation: Tensor(shape=torch.Size([5, 10, 176]), device=cpu, dtype=torch.float32, is_shared=False)},
                    batch_size=torch.Size([5, 10]),
                    device=cpu,
                    is_shared=False),
                done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                info: TensorDict(
                    fields={
                        battle_won: Tensor(shape=torch.Size([5]), device=cpu, dtype=torch.bool, is_shared=False),
                        dead_allies: Tensor(shape=torch.Size([5]), device=cpu, dtype=torch.int64, is_shared=False),
                        dead_enemies: Tensor(shape=torch.Size([5]), device=cpu, dtype=torch.int64, is_shared=False),
                        episode_limit: Tensor(shape=torch.Size([5]), device=cpu, dtype=torch.bool, is_shared=False)},
                    batch_size=torch.Size([5]),
                    device=cpu,
                    is_shared=False),
                reward: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                state: Tensor(shape=torch.Size([5, 322]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([5]),
            device=cpu,
            is_shared=False),
        state: Tensor(shape=torch.Size([5, 322]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([5]),
    device=cpu,
    is_shared=False)
>>> # Or the new features for procedural generation
>>> distribution_config = {
...     "n_units": 5,
...     "n_enemies": 6,
...     "team_gen": {
...         "dist_type": "weighted_teams",
...         "unit_types": ["marine", "marauder", "medivac"],
...         "exception_unit_types": ["medivac"],
...         "weights": [0.5, 0.2, 0.3],
...         "observe": True,
...     },
...     "start_positions": {
...         "dist_type": "surrounded_and_reflect",
...         "p": 0.5,
...         "n_enemies": 5,
...         "map_x": 32,
...         "map_y": 32,
...     },
... }
>>> env = SMACv2Env(
...     map_name="10gen_terran",
...     capability_config=distribution_config,
...     categorical_actions=False,
... )
>>> print(env.rollout(4))
TensorDict(
    fields={
        agents: TensorDict(
            fields={
                action: Tensor(shape=torch.Size([4, 5, 12]), device=cpu, dtype=torch.int64, is_shared=False),
                action_mask: Tensor(shape=torch.Size([4, 5, 12]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([4, 5, 88]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([4, 5]),
            device=cpu,
            is_shared=False),
        done: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        info: TensorDict(
            fields={
                battle_won: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.bool, is_shared=False),
                dead_allies: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.int64, is_shared=False),
                dead_enemies: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.int64, is_shared=False),
                episode_limit: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([4]),
            device=cpu,
            is_shared=False),
        next: TensorDict(
            fields={
                agents: TensorDict(
                    fields={
                        action_mask: Tensor(shape=torch.Size([4, 5, 12]), device=cpu, dtype=torch.bool, is_shared=False),
                        observation: Tensor(shape=torch.Size([4, 5, 88]), device=cpu, dtype=torch.float32, is_shared=False)},
                    batch_size=torch.Size([4, 5]),
                    device=cpu,
                    is_shared=False),
                done: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                info: TensorDict(
                    fields={
                        battle_won: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.bool, is_shared=False),
                        dead_allies: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.int64, is_shared=False),
                        dead_enemies: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.int64, is_shared=False),
                        episode_limit: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.bool, is_shared=False)},
                    batch_size=torch.Size([4]),
                    device=cpu,
                    is_shared=False),
                reward: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                state: Tensor(shape=torch.Size([4, 131]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([4]),
            device=cpu,
            is_shared=False),
        state: Tensor(shape=torch.Size([4, 131]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([4]),
    device=cpu,
    is_shared=False)