add yapf style and apply yapf to format all Python files

This recreates pull request #2192

1 files changed, 136 insertions, 135 deletions

diff --git a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py
index a1761bc30..32841eade 100644
--- a/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py
+++ b/examples/pybullet/gym/pybullet_envs/deep_mimic/learning/rl_world.py
@@ -5,139 +5,140 @@ from pybullet_envs.deep_mimic.learning.rl_agent import RLAgent
 from pybullet_utils.logger import Logger
 import pybullet_data
 
+
 class RLWorld(object):
-    def __init__(self, env, arg_parser):
-        TFUtil.disable_gpu()
-
-        self.env = env
-        self.arg_parser = arg_parser
-        self._enable_training = True
-        self.train_agents = []
-        self.parse_args(arg_parser)
-
-        self.build_agents()
-        
-        return
-
-    def get_enable_training(self):
-        return self._enable_training
-    
-    def set_enable_training(self, enable):
-        self._enable_training = enable
-        for i in range(len(self.agents)):
-            curr_agent = self.agents[i]
-            if curr_agent is not None:
-                enable_curr_train = self.train_agents[i] if (len(self.train_agents) > 0) else True
-                curr_agent.enable_training = self.enable_training and enable_curr_train
-
-        if (self._enable_training):
-            self.env.set_mode(RLAgent.Mode.TRAIN)
-        else:
-            self.env.set_mode(RLAgent.Mode.TEST)
-
-        return
-
-    enable_training = property(get_enable_training, set_enable_training)
-    
-    def parse_args(self, arg_parser):
-        self.train_agents = self.arg_parser.parse_bools('train_agents')
-        num_agents = self.env.get_num_agents()
-        assert(len(self.train_agents) == num_agents or len(self.train_agents) == 0)
-
-        return
-
-    def shutdown(self):
-        self.env.shutdown()
-        return
-
-    def build_agents(self):
-        num_agents = self.env.get_num_agents()
-        print("num_agents=",num_agents)
-        self.agents = []
-
-        Logger.print2('')
-        Logger.print2('Num Agents: {:d}'.format(num_agents))
-
-        agent_files = self.arg_parser.parse_strings('agent_files')
-        print("len(agent_files)=",len(agent_files))
-        assert(len(agent_files) == num_agents or len(agent_files) == 0)
-
-        model_files = self.arg_parser.parse_strings('model_files')
-        assert(len(model_files) == num_agents or len(model_files) == 0)
-
-        output_path = self.arg_parser.parse_string('output_path')
-        int_output_path = self.arg_parser.parse_string('int_output_path')
-
-        for i in range(num_agents):
-            curr_file = agent_files[i]
-            curr_agent = self._build_agent(i, curr_file)
-
-            if curr_agent is not None:
-                curr_agent.output_dir = output_path
-                curr_agent.int_output_dir = int_output_path
-                Logger.print2(str(curr_agent))
-
-                if (len(model_files) > 0):
-                    curr_model_file = model_files[i]
-                    if curr_model_file != 'none':
-                        curr_agent.load_model(pybullet_data.getDataPath()+"/"+curr_model_file)
-
-            self.agents.append(curr_agent)
-            Logger.print2('')
-
-        self.set_enable_training(self.enable_training)
-
-        return
-
-    def update(self, timestep):
-        #print("world update!\n")
-        self._update_agents(timestep)
-        self._update_env(timestep)
-        return
-
-    def reset(self):
-        self._reset_agents()
-        self._reset_env()
-        return
-
-    def end_episode(self):
-        self._end_episode_agents();
-        return
-
-    def _update_env(self, timestep):
-        self.env.update(timestep)
-        return
-
-    def _update_agents(self, timestep):
-        #print("len(agents)=",len(self.agents))
-        for agent in self.agents:
-            if (agent is not None):
-                agent.update(timestep)
-        return
-
-    def _reset_env(self):
-        self.env.reset()
-        return
-
-    def _reset_agents(self):
-        for agent in self.agents:
-            if (agent != None):
-                agent.reset()
-        return
-
-    def _end_episode_agents(self):
-        for agent in self.agents:
-            if (agent != None):
-                agent.end_episode()
-        return
-
-    def _build_agent(self, id, agent_file):
-        Logger.print2('Agent {:d}: {}'.format(id, agent_file))
-        if (agent_file == 'none'):
-            agent = None
-        else:
-            agent = AgentBuilder.build_agent(self, id, agent_file)
-            assert (agent != None), 'Failed to build agent {:d} from: {}'.format(id, agent_file)
-        
-        return agent
-        
+
+  def __init__(self, env, arg_parser):
+    TFUtil.disable_gpu()
+
+    self.env = env
+    self.arg_parser = arg_parser
+    self._enable_training = True
+    self.train_agents = []
+    self.parse_args(arg_parser)
+
+    self.build_agents()
+
+    return
+
+  def get_enable_training(self):
+    return self._enable_training
+
+  def set_enable_training(self, enable):
+    self._enable_training = enable
+    for i in range(len(self.agents)):
+      curr_agent = self.agents[i]
+      if curr_agent is not None:
+        enable_curr_train = self.train_agents[i] if (len(self.train_agents) > 0) else True
+        curr_agent.enable_training = self.enable_training and enable_curr_train
+
+    if (self._enable_training):
+      self.env.set_mode(RLAgent.Mode.TRAIN)
+    else:
+      self.env.set_mode(RLAgent.Mode.TEST)
+
+    return
+
+  enable_training = property(get_enable_training, set_enable_training)
+
+  def parse_args(self, arg_parser):
+    self.train_agents = self.arg_parser.parse_bools('train_agents')
+    num_agents = self.env.get_num_agents()
+    assert (len(self.train_agents) == num_agents or len(self.train_agents) == 0)
+
+    return
+
+  def shutdown(self):
+    self.env.shutdown()
+    return
+
+  def build_agents(self):
+    num_agents = self.env.get_num_agents()
+    print("num_agents=", num_agents)
+    self.agents = []
+
+    Logger.print2('')
+    Logger.print2('Num Agents: {:d}'.format(num_agents))
+
+    agent_files = self.arg_parser.parse_strings('agent_files')
+    print("len(agent_files)=", len(agent_files))
+    assert (len(agent_files) == num_agents or len(agent_files) == 0)
+
+    model_files = self.arg_parser.parse_strings('model_files')
+    assert (len(model_files) == num_agents or len(model_files) == 0)
+
+    output_path = self.arg_parser.parse_string('output_path')
+    int_output_path = self.arg_parser.parse_string('int_output_path')
+
+    for i in range(num_agents):
+      curr_file = agent_files[i]
+      curr_agent = self._build_agent(i, curr_file)
+
+      if curr_agent is not None:
+        curr_agent.output_dir = output_path
+        curr_agent.int_output_dir = int_output_path
+        Logger.print2(str(curr_agent))
+
+        if (len(model_files) > 0):
+          curr_model_file = model_files[i]
+          if curr_model_file != 'none':
+            curr_agent.load_model(pybullet_data.getDataPath() + "/" + curr_model_file)
+
+      self.agents.append(curr_agent)
+      Logger.print2('')
+
+    self.set_enable_training(self.enable_training)
+
+    return
+
+  def update(self, timestep):
+    #print("world update!\n")
+    self._update_agents(timestep)
+    self._update_env(timestep)
+    return
+
+  def reset(self):
+    self._reset_agents()
+    self._reset_env()
+    return
+
+  def end_episode(self):
+    self._end_episode_agents()
+    return
+
+  def _update_env(self, timestep):
+    self.env.update(timestep)
+    return
+
+  def _update_agents(self, timestep):
+    #print("len(agents)=",len(self.agents))
+    for agent in self.agents:
+      if (agent is not None):
+        agent.update(timestep)
+    return
+
+  def _reset_env(self):
+    self.env.reset()
+    return
+
+  def _reset_agents(self):
+    for agent in self.agents:
+      if (agent != None):
+        agent.reset()
+    return
+
+  def _end_episode_agents(self):
+    for agent in self.agents:
+      if (agent != None):
+        agent.end_episode()
+    return
+
+  def _build_agent(self, id, agent_file):
+    Logger.print2('Agent {:d}: {}'.format(id, agent_file))
+    if (agent_file == 'none'):
+      agent = None
+    else:
+      agent = AgentBuilder.build_agent(self, id, agent_file)
+      assert (agent != None), 'Failed to build agent {:d} from: {}'.format(id, agent_file)
+
+    return agent