fix norm

ddpg/model.py

@@ -216,7 +216,7 @@ def train_multi_thread(actor, critic, dynamics, target_actor, target_critic, tar
     env = create_env(args)
     random_process = create_random_process(args)
 
-    state_normalizer = StaticNormalizer(env.state_dim)
+    state_normalizer = StaticNormalizer(int(np.prod(env.observation_space.shape)))
     reward_normalizer = StaticNormalizer(1)
 
     actor_learning_rate_decay_fn = create_decay_fn(
@@ -488,7 +488,7 @@ def play_single_thread(
         num_cycles=args.max_episodes // epsilon_cycle_len)
 
     env = create_env(args)
-    state_normalizer = StaticNormalizer(env.state_dim)
+    state_normalizer = StaticNormalizer(int(np.prod(env.observation_space.shape)))
     reward_normalizer = StaticNormalizer(1)
 
     episode = 1

ddpg/train.py

@@ -5,10 +5,12 @@ import copy
 import torch
 import torch.multiprocessing as mp
 from multiprocessing import Value
+import numpy as np
 
 from common.misc_util import boolean_flag, str2params, create_if_need
 from common.env_wrappers import create_env
 from common.torch_util import activations, hard_update
+from common.normalizer import StaticNormalizer
 
 from ddpg.model import create_model, create_act_update_fns, train_multi_thread, \
     train_single_thread, play_single_thread
@@ -148,7 +150,7 @@ def train(args, model_fn, act_update_fns, multi_thread, train_single, play_singl
         json.dump(vars(args), fout, indent=4, ensure_ascii=False, sort_keys=True)
 
     env = create_env(args)
-    shared_state_normalizer = StaticNormalizer(env.state_dim)
+    shared_state_normalizer = StaticNormalizer(int(np.prod(env.observation_space.shape)))
     shared_reward_normalizer = StaticNormalizer(1)
 
     if args.flip_state_action and hasattr(env, "state_transform"):