temp commit

configurations.py

@@ -69,7 +69,7 @@ class Parameters(object):
                 parameters[k] = dict(v.items())
             else:
                 parameters[k] = v
-                
+
         return json.dumps(parameters, indent=4, default=repr)
 
 
@@ -77,7 +77,7 @@ class AgentParameters(Parameters):
     agent = ''
 
     # Architecture parameters
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.Q]
     middleware_type = MiddlewareTypes.FC
     loss_weights = [1.0]
@@ -327,7 +327,7 @@ class Human(AgentParameters):
 
 class NStepQ(AgentParameters):
     type = 'NStepQAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.Q]
     loss_weights = [1.0]
     optimizer_type = 'Adam'
@@ -343,7 +343,7 @@ class NStepQ(AgentParameters):
 
 class DQN(AgentParameters):
     type = 'DQNAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.Q]
     loss_weights = [1.0]
     optimizer_type = 'Adam'
@@ -385,7 +385,7 @@ class QuantileRegressionDQN(DQN):
 class NEC(AgentParameters):
     type = 'NECAgent'
     optimizer_type = 'RMSProp'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.DNDQ]
     loss_weights = [1.0]
     dnd_size = 500000
@@ -399,7 +399,7 @@ class NEC(AgentParameters):
 
 class ActorCritic(AgentParameters):
     type = 'ActorCriticAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.V, OutputTypes.Pi]
     loss_weights = [0.5, 1.0]
     stop_gradients_from_head = [False, False]
@@ -417,7 +417,7 @@ class ActorCritic(AgentParameters):
 
 class PolicyGradient(AgentParameters):
     type = 'PolicyGradientsAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.Pi]
     loss_weights = [1.0]
     num_episodes_in_experience_replay = 2
@@ -430,7 +430,7 @@ class PolicyGradient(AgentParameters):
 
 class DDPG(AgentParameters):
     type = 'DDPGAgent'
-    input_types = [InputTypes.Observation, InputTypes.Action]
+    input_types = {'observation': InputTypes.Observation, 'action': InputTypes.Action}
     output_types = [OutputTypes.V]  # V is used because we only want a single Q value
     loss_weights = [1.0]
     hidden_layers_activation_function = 'relu'
@@ -443,7 +443,7 @@ class DDPG(AgentParameters):
 
 class DDDPG(AgentParameters):
     type = 'DDPGAgent'
-    input_types = [InputTypes.Observation, InputTypes.Action]
+    input_types = {'observation': InputTypes.Observation, 'action': InputTypes.Action}
     output_types = [OutputTypes.V]  # V is used because we only want a single Q value
     loss_weights = [1.0]
     hidden_layers_activation_function = 'relu'
@@ -456,7 +456,7 @@ class DDDPG(AgentParameters):
 
 class NAF(AgentParameters):
     type = 'NAFAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.NAF]
     loss_weights = [1.0]
     hidden_layers_activation_function = 'tanh'
@@ -469,7 +469,7 @@ class NAF(AgentParameters):
 
 class PPO(AgentParameters):
     type = 'PPOAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.V]
     loss_weights = [1.0]
     hidden_layers_activation_function = 'tanh'
@@ -489,7 +489,7 @@ class PPO(AgentParameters):
 
 class ClippedPPO(AgentParameters):
     type = 'ClippedPPOAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.V, OutputTypes.PPO]
     loss_weights = [0.5, 1.0]
     stop_gradients_from_head = [False, False]
@@ -515,7 +515,11 @@ class ClippedPPO(AgentParameters):
 
 class DFP(AgentParameters):
     type = 'DFPAgent'
-    input_types = [InputTypes.Observation, InputTypes.Measurements, InputTypes.GoalVector]
+    input_types = {
+        'observation': InputTypes.Observation,
+        'measurements': InputTypes.Measurements,
+        'goal': InputTypes.GoalVector
+    }
     output_types = [OutputTypes.MeasurementsPrediction]
     loss_weights = [1.0]
     use_measurements = True
@@ -527,7 +531,7 @@ class DFP(AgentParameters):
 
 class MMC(AgentParameters):
     type = 'MixedMonteCarloAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.Q]
     loss_weights = [1.0]
     num_steps_between_copying_online_weights_to_target = 1000
@@ -537,7 +541,7 @@ class MMC(AgentParameters):
 
 class PAL(AgentParameters):
     type = 'PALAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.Q]
     loss_weights = [1.0]
     pal_alpha = 0.9
@@ -548,7 +552,7 @@ class PAL(AgentParameters):
 
 class BC(AgentParameters):
     type = 'BCAgent'
-    input_types = [InputTypes.Observation]
+    input_types = {'observation': InputTypes.Observation}
     output_types = [OutputTypes.Q]
     loss_weights = [1.0]
     collect_new_data = False