enable deepmimic training on mac

examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py

@@ -22,8 +22,10 @@ class PyBulletDeepMimicEnv(Env):
     def reset(self):
       self.t = 0
       if not self._isInitialized:
-        self._pybullet_client =  bullet_client.BulletClient(connection_mode=p1.GUI)
-        #self._pybullet_client  =  bullet_client.BulletClient()
+        if self.enable_draw:
+          self._pybullet_client =  bullet_client.BulletClient(connection_mode=p1.GUI)
+        else:
+          self._pybullet_client  =  bullet_client.BulletClient()
         
         self._pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath())
         z2y = self._pybullet_client.getQuaternionFromEuler([-math.pi*0.5,0,0])
@@ -198,7 +200,7 @@ class PyBulletDeepMimicEnv(Env):
         state = self._humanoid.getState()
         state[1]=state[1]+0.008
         #print("record_state=",state)
-        return state
+        return np.array(state)
         
         
     def record_goal(self, agent_id):
@@ -241,7 +243,7 @@ class PyBulletDeepMimicEnv(Env):
     def is_episode_end(self):
       isEnded = self._humanoid.terminates()
       #also check maximum time, 20 seconds (todo get from file)
-      print("self.t=",self.t)
+      #print("self.t=",self.t)
       if (self.t>3):
         isEnded = True
       return isEnded

examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py

@@ -1,12 +1,13 @@
 import json
 import numpy as np
 from learning.ppo_agent import PPOAgent
+import pybullet_data
 
 AGENT_TYPE_KEY = "AgentType"
 
 def build_agent(world, id, file):
     agent = None
-    with open(file) as data_file:    
+    with open(pybullet_data.getDataPath()+"/"+file) as data_file:    
         json_data = json.load(data_file)
         
         assert AGENT_TYPE_KEY in json_data

examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py

@@ -2,7 +2,7 @@ import numpy as np
 import copy
 from pybullet_utils.logger import Logger
 import inspect as inspect
-from env.env import Env
+from pybullet_envs.deep_mimic.env.env import Env
 import pybullet_utils.math_util as MathUtil
 
 class ReplayBuffer(object):

examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py

@@ -1,5 +1,11 @@
+import os
+import inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(os.path.dirname(currentdir))
+os.sys.path.insert(0,parentdir)
+print("parentdir=",parentdir)
 import json
-from learning.rl_world import RLWorld
+from pybullet_envs.deep_mimic.learning.rl_world import RLWorld
 from learning.ppo_agent import PPOAgent
 
 import pybullet_data
@@ -36,7 +42,7 @@ print("bodies=",bodies)
 int_output_path = arg_parser.parse_string("int_output_path")
 print("int_output_path=",int_output_path)
 
-agent_files = arg_parser.parse_string("agent_files")
+agent_files = pybullet_data.getDataPath()+"/"+arg_parser.parse_string("agent_files")
 
 AGENT_TYPE_KEY = "AgentType"