Rename actions_input to action_mask

run.py

@@ -35,8 +35,8 @@ def one_hot_encode(n, action):
 
 
 def predict(env, model, observations):
- actions_input = np.ones((len(observations), env.action_space.n))
- return model.predict(x=[observations, actions_input])
+ action_mask = np.ones((len(observations), env.action_space.n))
+ return model.predict(x=[observations, action_mask])
 
 
 def fit_batch(env, model, target_model, batch):
@@ -47,7 +47,6 @@ def fit_batch(env, model, target_model, batch):
  next_q_values[dones] = 0.0
  # The Q values of each start state is the reward + gamma * the max next state Q value
  q_values = rewards + DISCOUNT_FACTOR_GAMMA * np.max(next_q_values, axis=1)
- # Passing the actions as the mask and multiplying the targets by the actions masks.
  one_hot_actions = np.array([one_hot_encode(env.action_space.n, action) for action in actions])
  history = model.fit(
  x=[observations, one_hot_actions],
@@ -62,12 +61,12 @@ def create_model(env):
  n_actions = env.action_space.n
  obs_shape = env.observation_space.shape
  observations_input = keras.layers.Input(obs_shape, name='observations_input')
- actions_input = keras.layers.Input((n_actions,), name='actions_input')
+ action_mask = keras.layers.Input((n_actions,), name='action_mask')
  hidden = keras.layers.Dense(32, activation='relu')(observations_input)
  hidden_2 = keras.layers.Dense(32, activation='relu')(hidden)
  output = keras.layers.Dense(n_actions)(hidden_2)
- filtered_output = keras.layers.multiply([output, actions_input])
- model = keras.models.Model([observations_input, actions_input], filtered_output)
+ filtered_output = keras.layers.multiply([output, action_mask])
+ model = keras.models.Model([observations_input, action_mask], filtered_output)
  optimizer = keras.optimizers.Adam(lr=LEARNING_RATE, clipnorm=1.0)
  model.compile(optimizer, loss='mean_squared_error')
  return model