fixed typo

Author: ryan-lowe

multiagent/environment.py

@@ -13,14 +13,13 @@ class MultiAgentEnv(gym.Env):
  }
 
  def __init__(self, world, reset_callback=None, reward_callback=None,
- observation_callback=None, arglist=None, info_callback=None,
+ observation_callback=None, info_callback=None,
  shared_viewer=True):
 
  self.world = world
  self.agents = self.world.policy_agents
- self.arglist = arglist
  # set required vectorized gym env property
- self.n = len(world.policy_agents) 
+ self.n = len(world.policy_agents)
  # scenario callbacks
  self.reset_callback = reset_callback
  self.reward_callback = reward_callback
@@ -65,27 +64,25 @@ def __init__(self, world, reset_callback=None, reward_callback=None,
  self.action_space.append(act_space)
  else:
  self.action_space.append(total_action_space[0])
- # observation space 
+ # observation space
  obs_dim = len(observation_callback(agent, self.world))
  self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim),))
  agent.action.c = np.zeros(self.world.dim_c)
 
- 
+
  # rendering
  self.shared_viewer = shared_viewer
  if self.shared_viewer:
  self.viewers = [None]
  else:
- self.viewers = [None] * self.n 
+ self.viewers = [None] * self.n
  self._reset_render()
 
  def _step(self, action_n):
  obs_n = []
  reward_n = []
  done_n = []
  info_n = {'n': []}
- if self.arglist and self.arglist.render_each_step:
- self._reset_render()
  self.agents = self.world.policy_agents
  # set action for each agent
  for i, agent in enumerate(self.agents):
@@ -149,7 +146,7 @@ def _set_action(self, action, agent, action_space, time=None):
  for s in size:
  act.append(action[index:(index+s)])
  index += s
- action = act 
+ action = act
  else:
  action = [action]
 
@@ -187,7 +184,7 @@ def _set_action(self, action, agent, action_space, time=None):
  assert len(action) == 0
 
  # reset rendering assets
- def _reset_render(self): 
+ def _reset_render(self):
  self.render_geoms = None
  self.render_geoms_xform = None
 
@@ -261,7 +258,7 @@ def _render(self, mode='human', close=True):
  else:
  pos = self.agents[i].state.p_pos
  self.viewers[i].set_bounds(pos[0]-cam_range,pos[0]+cam_range,pos[1]-cam_range,pos[1]+cam_range)
- # update geometry positions 
+ # update geometry positions
  for e, entity in enumerate(self.world.entities):
  self.render_geoms_xform[e].set_translation(*entity.state.p_pos)
  # render to display or array
@@ -297,13 +294,13 @@ def _get_receptor_activations(self, entity_root, state_root, other_x_pos):
  activations.append(tf.reduce_sum(tf.square(dx_pos), 1, keep_dims=True))
  activations = tf.concat(1, activations)
  #return tf.nn.softmax(-1e+1 * activations)
- return tf.exp(-activations / 1.0**2) 
+ return tf.exp(-activations / 1.0**2)
 
 # vectorized wrapper for a batch of multi-agent environments
 # assumes all environments have the same observation and action space
 class BatchMultiAgentEnv(gym.Env):
  metadata = {
- 'runtime.vectorized': True, 
+ 'runtime.vectorized': True,
  'render.modes' : ['human', 'rgb_array']
  }
 
@@ -320,7 +317,7 @@ def action_space(self):
 
  @property
  def observation_space(self):
- return self.env_batch[0].observation_space 
+ return self.env_batch[0].observation_space
 
  def _step(self, action_n, time):
  obs_n = []
@@ -348,4 +345,4 @@ def _render(self, mode='human', close=True):
  results_n = []
  for env in self.env_batch:
  results_n += env.render(mode, close)
- return results_n 
+ return results_n