fixed some interactive.py bugs

bin/interactive.py

@@ -17,7 +17,7 @@
     scenario = scenarios.load(args.scenario).Scenario()
     # create world
     world = scenario.make_world()
-    # create multiagent environment 
+    # create multiagent environment
     env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, shared_viewer = False)
     # render call to create viewer window (necessary only for interactive policies)
     env.render()
@@ -33,7 +33,7 @@
         # step environment
         obs_n, reward_n, done_n, _ = env.step(act_n)
         # render all agent views
-        env.render() 
-        # display rewards   
-        for agent in env.world.agents:
-            print(agent.name + " reward: %0.3f" % env._get_reward(agent))
+        env.render()
+        # display rewards
+        #for agent in env.world.agents:
+        #    print(agent.name + " reward: %0.3f" % env._get_reward(agent))

multiagent/environment.py

@@ -70,7 +70,6 @@ def __init__(self, world, reset_callback=None, reward_callback=None,
             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:
@@ -90,7 +89,7 @@ def _step(self, action_n):
             self._set_action(action_n[i], agent, self.action_space[i])
         # advance world state
         self.world.step()
-        # record observation for each agent  # TODO: clean up
+        # record observation for each agent
         for agent in self.agents:
             obs_n.append(self._get_obs(agent))
             reward_n.append(self._get_reward(agent))
@@ -147,12 +146,13 @@ def _set_action(self, action, agent, action_space, time=None):
                 act.append(action[index:(index+s)])
                 index += s
             action = act
-        else:
-            action = [action]
+        #else:
+        #    action = [action]  # TODO: why is this necessary??
 
         if agent.movable:
             # physical action
             if self.discrete_action_input:
+                print(action)
                 agent.action.u = np.zeros(self.world.dim_p)
                 # process discrete action
                 if action[0] == 1: agent.action.u[0] = -1.0

multiagent/policy.py

@@ -13,33 +13,33 @@ class InteractivePolicy(Policy):
     def __init__(self, env, agent_index):
         super(InteractivePolicy, self).__init__()
         self.env = env
-        # hard-coded keyborad events
+        # hard-coded keyboard events
         self.move = [False for i in range(4)]
         self.comm = [False for i in range(env.world.dim_c)]
-        # register keyboard events with this envornment's window
+        # register keyboard events with this environment's window
         env.viewers[agent_index].window.on_key_press = self.key_press
         env.viewers[agent_index].window.on_key_release = self.key_release
 
     def action(self, obs):
         # ignore observation and just act based on keyboard events
-        if self.env.discrete_action_space:
+        if self.env.discrete_action_input:
             u = 0
             if self.move[0]: u = 1
             if self.move[1]: u = 2
             if self.move[2]: u = 4
             if self.move[3]: u = 3
         else:
-            u = np.array([0.0,0.0])
-            if self.move[0]: u[0] -= 1.0
-            if self.move[1]: u[0] += 1.0
-            if self.move[2]: u[1] += 1.0
-            if self.move[3]: u[1] -= 1.0
+            u = np.array([0.0,0.0,0.0,0.0])
+            if self.move[0]: u[0] += 1.0
+            if self.move[1]: u[1] += 1.0
+            if self.move[2]: u[2] += 1.0
+            if self.move[3]: u[3] += 1.0
         c = 0
         for i in range(len(self.comm)):
             if self.comm[i]: c = i+1
         return [u, c]
 
-    # keyborad event callbacks 
+    # keyborad event callbacks
     def key_press(self, k, mod):
         if k==key.LEFT:  self.move[0] = True
         if k==key.RIGHT: self.move[1] = True
@@ -53,4 +53,4 @@ def key_release(self, k, mod):
         if k==key.UP:    self.move[2] = False
         if k==key.DOWN:  self.move[3] = False
         for i in range(len(self.comm)):
-            if k==key._1+i:  self.comm[i] = False
+            if k==key._1+i:  self.comm[i] = False

multiagent/scenarios/simple.py

@@ -10,7 +10,7 @@ def make_world(self):
         for i, agent in enumerate(world.agents):
             agent.name = 'agent %d' % i
             agent.collide = False
-            agent.silent = True  
+            agent.silent = True
         # add landmarks
         world.landmarks = [Landmark() for i in range(1)]
         for i, landmark in enumerate(world.landmarks):
@@ -24,11 +24,11 @@ def make_world(self):
     def reset_world(self, world):
         # random properties for agents
         for i, agent in enumerate(world.agents):
-            agent.color = np.array([0.25,0.25,0.25])               
+            agent.color = np.array([0.25,0.25,0.25])
         # random properties for landmarks
         for i, landmark in enumerate(world.landmarks):
             landmark.color = np.array([0.75,0.75,0.75])
-        world.landmarks[0].color = np.array([0.75,0.25,0.25])                     
+        world.landmarks[0].color = np.array([0.75,0.25,0.25])
         # set random initial states
         for agent in world.agents:
             agent.state.p_pos = np.random.uniform(-1,+1, world.dim_p)
@@ -42,10 +42,9 @@ def reward(self, agent, world):
         dist2 = np.sum(np.square(agent.state.p_pos - world.landmarks[0].state.p_pos))
         return -dist2 #np.exp(-dist2)
 
-    def observation(self, agent, world):        
+    def observation(self, agent, world):
         # get positions of all entities in this agent's reference frame
         entity_pos = []
         for entity in world.landmarks:
             entity_pos.append(entity.state.p_pos - agent.state.p_pos)
         return np.concatenate([agent.state.p_vel] + entity_pos)
-

setup.py

@@ -3,7 +3,7 @@
 setup(name='multiagent',
       version='0.0.1',
       description='Multi-Agent Goal-Driven Communication Environment',
-      url='https://github.com/openai/multiagent',
+      url='https://github.com/openai/multiagent-public',
       author='Igor Mordatch',
       author_email='mordatch@openai.com',
       packages=find_packages(),