utils.py

import tensorflow as tf

def layer(input_layer, num_next_neurons, is_output=False):
    num_prev_neurons = int(input_layer.shape[1])
    shape = [num_prev_neurons, num_next_neurons]
    
    if is_output:
        weight_init = tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3)
        bias_init = tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3)
    else:
        # 1/sqrt(f)
        fan_in_init = 1 / num_prev_neurons ** 0.5
        weight_init = tf.random_uniform_initializer(minval=-fan_in_init, maxval=fan_in_init)
        bias_init = tf.random_uniform_initializer(minval=-fan_in_init, maxval=fan_in_init) 

    weights = tf.get_variable("weights", shape, initializer=weight_init)
    biases = tf.get_variable("biases", [num_next_neurons], initializer=bias_init)

    dot = tf.matmul(input_layer, weights) + biases

    if is_output:
        return dot

    relu = tf.nn.relu(dot)
    return relu

def layer_goal_nn(input_layer, num_next_neurons, is_output=False):
    num_prev_neurons = int(input_layer.shape[1])
    shape = [num_prev_neurons, num_next_neurons]
    
    
    fan_in_init = 1 / num_prev_neurons ** 0.5
    weight_init = tf.random_uniform_initializer(minval=-fan_in_init, maxval=fan_in_init)
    bias_init = tf.random_uniform_initializer(minval=-fan_in_init, maxval=fan_in_init) 

    weights = tf.get_variable("weights", shape, initializer=weight_init)
    biases = tf.get_variable("biases", [num_next_neurons], initializer=bias_init)

    dot = tf.matmul(input_layer, weights) + biases

    if is_output:
        return dot

    relu = tf.nn.relu(dot)
    return relu


# Below function prints out options and environment specified by user
def print_summary(FLAGS,env):

    print("\n- - - - - - - - - - -")
    print("Task Summary: ","\n")
    print("Environment: ", env.name)
    print("Number of Layers: ", FLAGS.layers)
    print("Time Limit per Layer: ", FLAGS.time_scale)
    print("Max Episode Time Steps: ", env.max_actions)
    print("Retrain: ", FLAGS.retrain)
    print("Test: ", FLAGS.test)
    print("Visualize: ", FLAGS.show)
    print("- - - - - - - - - - -", "\n\n")


# Below function ensures environment configurations were properly entered
def check_validity(model_name, goal_space_train, goal_space_test, end_goal_thresholds, initial_state_space, subgoal_bounds, subgoal_thresholds, max_actions, timesteps_per_action):

    # Ensure model file is an ".xml" file
    assert model_name[-4:] == ".xml", "Mujoco model must be an \".xml\" file"

    # Ensure upper bounds of range is >= lower bound of range
    if goal_space_train is not None:
        for i in range(len(goal_space_train)):
            assert goal_space_train[i][1] >= goal_space_train[i][0], "In the training goal space, upper bound must be >= lower bound"

    if goal_space_test is not None:
        for i in range(len(goal_space_test)):
            assert goal_space_test[i][1] >= goal_space_test[i][0], "In the training goal space, upper bound must be >= lower bound"

    for i in range(len(initial_state_space)):
        assert initial_state_space[i][1] >= initial_state_space[i][0], "In initial state space, upper bound must be >= lower bound"
    
    for i in range(len(subgoal_bounds)):
        assert subgoal_bounds[i][1] >= subgoal_bounds[i][0], "In subgoal space, upper bound must be >= lower bound" 

    # Make sure end goal spaces and thresholds have same first dimension
    if goal_space_train is not None and goal_space_test is not None:
        assert len(goal_space_train) == len(goal_space_test) == len(end_goal_thresholds), "End goal space and thresholds must have same first dimension"

    # Makde sure suboal spaces and thresholds have same dimensions
    assert len(subgoal_bounds) == len(subgoal_thresholds), "Subgoal space and thresholds must have same first dimension"

    # Ensure max action and timesteps_per_action are postive integers
    assert max_actions > 0, "Max actions should be a positive integer"

    assert timesteps_per_action > 0, "Timesteps per action should be a positive integer"