multiple bug fixes in dealing with measurements + CartPole_DFP preset (#92)

agents/agent.py

@@ -365,7 +365,10 @@ class Agent(object):
             'observation': observation
         }
         if self.tp.agent.use_measurements:
-            self.curr_state['measurements'] = self.env.measurements
+            if 'measurements' in self.curr_state.keys():
+                self.curr_state['measurements'] = self.env.state['measurements']
+            else:
+                self.curr_state['measurements'] = np.zeros(0)
             if self.tp.agent.use_accumulated_reward_as_measurement:
                 self.curr_state['measurements'] = np.append(self.curr_state['measurements'], 0)
 
@@ -398,7 +401,7 @@ class Agent(object):
             shaped_reward += action_info['action_intrinsic_reward']
         # TODO: should total_reward_in_current_episode include shaped_reward?
         self.total_reward_in_current_episode += result['reward']
-        next_state = result['state']
+        next_state = copy.copy(result['state'])
         next_state['observation'] = self.preprocess_observation(next_state['observation'])
 
         # plot action values online
@@ -411,8 +414,11 @@ class Agent(object):
         observation = LazyStack(self.curr_stack, -1)
 
         next_state['observation'] = observation
-        if self.tp.agent.use_measurements and 'measurements' in result.keys():
+        if self.tp.agent.use_measurements:
+            if 'measurements' in result['state'].keys():
                 next_state['measurements'] = result['state']['measurements']
+            else:
+                next_state['measurements'] = np.zeros(0)
             if self.tp.agent.use_accumulated_reward_as_measurement:
                 next_state['measurements'] = np.append(next_state['measurements'], self.total_reward_in_current_episode)
 

agents/dfp_agent.py

@@ -31,7 +31,7 @@ class DFPAgent(Agent):
 
         # create the inputs for the network
         input = current_states
-        input.append(np.repeat(np.expand_dims(self.current_goal, 0), self.tp.batch_size, 0))
+        input['goal'] = np.repeat(np.expand_dims(self.current_goal, 0), self.tp.batch_size, 0)
 
         # get the current outputs of the network
         targets = self.main_network.online_network.predict(input)
@@ -40,7 +40,7 @@ class DFPAgent(Agent):
         for i in range(self.tp.batch_size):
             targets[i, actions[i]] = batch[i].info['future_measurements'].flatten()
 
-        result = self.main_network.train_and_sync_networks(current_states, targets)
+        result = self.main_network.train_and_sync_networks(input, targets)
         total_loss = result[0]
 
         return total_loss
@@ -52,7 +52,10 @@ class DFPAgent(Agent):
         goal = np.expand_dims(self.current_goal, 0)
 
         # predict the future measurements
-        measurements_future_prediction = self.main_network.online_network.predict([observation, measurements, goal])[0]
+        measurements_future_prediction = self.main_network.online_network.predict({
+            "observation": observation,
+            "measurements": measurements,
+            "goal": goal})[0]
         action_values = np.zeros((self.action_space_size,))
         num_steps_used_for_objective = len(self.tp.agent.future_measurements_weights)
 

environments/doom_environment_wrapper.py

@@ -135,7 +135,7 @@ class DoomEnvironmentWrapper(EnvironmentWrapper):
         # extract all data from the current state
         state = self.game.get_state()
         if state is not None and state.screen_buffer is not None:
-            self.observation = {
+            self.state = {
                 'observation': state.screen_buffer,
                 'measurements': state.game_variables,
             }

environments/gym_environment_wrapper.py

@@ -113,7 +113,7 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
             self.timestep_limit = self.env.spec.timestep_limit
         else:
             self.timestep_limit = None
-        self.measurements_size = len(self.step(0)['info'].keys())
+        self.measurements_size = (len(self.step(0)['info'].keys()),)
         self.random_initialization_steps = self.tp.env.random_initialization_steps
 
     def _wrap_state(self, state):

presets.py

@@ -200,6 +200,19 @@ class CartPole_PAL(Preset):
         self.test_max_step_threshold = 100
         self.test_min_return_threshold = 150
 
+
+class CartPole_DFP(Preset):
+    def __init__(self):
+        Preset.__init__(self, DFP, GymVectorObservation, ExplorationParameters)
+        self.env.level = 'CartPole-v0'
+        self.agent.num_episodes_in_experience_replay = 200
+        self.learning_rate = 0.0001
+        self.num_heatup_steps = 1000
+        self.exploration.epsilon_decay_steps = 10000
+        self.agent.use_accumulated_reward_as_measurement = True
+        self.agent.goal_vector = [1.0]
+
+
 class Doom_Basic_DFP(Preset):
     def __init__(self):
         Preset.__init__(self, DFP, Doom, ExplorationParameters)