modifi filepath

__init__.py

-from mappo import algorithms, envs, runner, scripts, utils, config
+from . import algorithms, envs, runner, scripts, utils, config
 
 
 __version__ = "0.1.0"

algorithms/algorithm/rMAPPOPolicy.py

@@ -6,8 +6,8 @@
 """
 
 import torch
-from mappo.algorithms.algorithm.r_actor_critic import R_Actor, R_Critic
-from mappo.utils.util import update_linear_schedule
+from algorithms.algorithm.r_actor_critic import R_Actor, R_Critic
+from utils.util import update_linear_schedule
 
 
 class RMAPPOPolicy:

algorithms/algorithm/r_actor_critic.py

@@ -7,13 +7,13 @@
 
 import torch
 import torch.nn as nn
-from mappo.algorithms.utils.util import init, check
-from mappo.algorithms.utils.cnn import CNNBase
-from mappo.algorithms.utils.mlp import MLPBase
-from mappo.algorithms.utils.rnn import RNNLayer
-from mappo.algorithms.utils.act import ACTLayer
-from mappo.algorithms.utils.popart import PopArt
-from mappo.utils.util import get_shape_from_obs_space
+from algorithms.utils.util import init, check
+from algorithms.utils.cnn import CNNBase
+from algorithms.utils.mlp import MLPBase
+from algorithms.utils.rnn import RNNLayer
+from algorithms.utils.act import ACTLayer
+from algorithms.utils.popart import PopArt
+from utils.util import get_shape_from_obs_space
 
 
 class R_Actor(nn.Module):

algorithms/algorithm/r_mappo.py

@@ -8,9 +8,9 @@
 import numpy as np
 import torch
 import torch.nn as nn
-from mappo.utils.util import get_gard_norm, huber_loss, mse_loss
-from mappo.utils.valuenorm import ValueNorm
-from mappo.algorithms.utils.util import check
+from utils.util import get_gard_norm, huber_loss, mse_loss
+from utils.valuenorm import ValueNorm
+from algorithms.utils.util import check
 
 
 class RMAPPO():

config.py

@@ -189,7 +189,7 @@ def get_config():
 
     # network parameters
     parser.add_argument("--share_policy", action='store_false',
-                        default=False, help='Whether agent share the same policy')
+                        default=True, help='Whether agent share the same policy')
     parser.add_argument("--use_centralized_V", action='store_false',
                         default=True, help="Whether to use centralized V function")
     parser.add_argument("--stacked_frames", type=int, default=1,

runner/separated/base_runner.py

@@ -7,8 +7,8 @@ from itertools import chain
 import torch
 from tensorboardX import SummaryWriter
 
-from mappo.utils.separated_buffer import SeparatedReplayBuffer
-from mappo.utils.util import update_linear_schedule
+from utils.separated_buffer import SeparatedReplayBuffer
+from utils.util import update_linear_schedule
 
 def _t2n(x):
     return x.detach().cpu().numpy()
@@ -67,8 +67,8 @@ class Runner(object):
                     os.makedirs(self.save_dir)
 
 
-        from mappo.algorithms.algorithm.r_mappo import RMAPPO as TrainAlgo
-        from mappo.algorithms.algorithm.rMAPPOPolicy import RMAPPOPolicy as Policy
+        from algorithms.algorithm.r_mappo import RMAPPO as TrainAlgo
+        from algorithms.algorithm.rMAPPOPolicy import RMAPPOPolicy as Policy
 
 
         self.policy = []

runner/separated/env_runner.py

@@ -12,8 +12,8 @@ import numpy as np
 from itertools import chain
 import torch
 
-from mappo.utils.util import update_linear_schedule
-from mappo.runner.separated.base_runner import Runner
+from utils.util import update_linear_schedule
+from runner.separated.base_runner import Runner
 import imageio
 
 

runner/shared/base_runner.py

@@ -3,7 +3,7 @@ import os
 import numpy as np
 import torch
 from tensorboardX import SummaryWriter
-from mappo.utils.shared_buffer import SharedReplayBuffer
+from utils.shared_buffer import SharedReplayBuffer
 
 def _t2n(x):
     """Convert torch tensor to a numpy array."""
@@ -63,8 +63,8 @@ class Runner(object):
             if not os.path.exists(self.save_dir):
                 os.makedirs(self.save_dir)
 
-        from mappo.algorithms.algorithm.r_mappo import RMAPPO as TrainAlgo
-        from mappo.algorithms.algorithm.rMAPPOPolicy import RMAPPOPolicy as Policy
+        from algorithms.algorithm.r_mappo import RMAPPO as TrainAlgo
+        from algorithms.algorithm.rMAPPOPolicy import RMAPPOPolicy as Policy
 
         share_observation_space = self.envs.share_observation_space[0] if self.use_centralized_V else self.envs.observation_space[0]
 

runner/shared/env_runner.py

@@ -15,7 +15,7 @@
 import time
 import numpy as np
 import torch
-from mappo.runner.shared.base_runner import Runner
+from runner.shared.base_runner import Runner
 import wandb
 import imageio
 

train/train.py

@@ -15,7 +15,7 @@ import numpy as np
 from pathlib import Path
 import torch
 from config import get_config
-from .envs.env_wrappers import SubprocVecEnv, DummyVecEnv
+from envs.env_wrappers import SubprocVecEnv, DummyVecEnv
 
 """Train script for MPEs."""
 
@@ -124,9 +124,9 @@ def main(args):
 
     # run experiments
     if all_args.share_policy:
-        from mappo.runner.shared.env_runner import EnvRunner as Runner
+        from runner.shared.env_runner import EnvRunner as Runner
     else:
-        from mappo.runner.separated.env_runner import EnvRunner as Runner
+        from runner.separated.env_runner import EnvRunner as Runner
 
     runner = Runner(config)
     runner.run()

utils/separated_buffer.py

@@ -2,7 +2,7 @@ import torch
 import numpy as np
 from collections import defaultdict
 
-from mappo.utils.util import check, get_shape_from_obs_space, get_shape_from_act_space
+from utils.util import check, get_shape_from_obs_space, get_shape_from_act_space
 
 def _flatten(T, N, x):
     return x.reshape(T * N, *x.shape[2:])

utils/shared_buffer.py

 import torch
 import numpy as np
-from mappo.utils.util import get_shape_from_obs_space, get_shape_from_act_space
+from utils.util import get_shape_from_obs_space, get_shape_from_act_space
 
 
 def _flatten(T, N, x):