SMACv2Env¶
- torchrl.envs.SMACv2Env(*args, **kwargs)[source]¶
SMACv2 (StarCraft Multi-Agent Challenge v2) environment wrapper.
To install the environment follow the following guide.
Examples
>>> 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)
