openai / maddpg - Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Learn more...

Conditional Complexity Overall

There are 159 units with 727 lines of code in units (91.1% of code).

0 very complex units (0 lines of code)
1 complex units (85 lines of code)
0 medium complex units (0 lines of code)
2 simple units (52 lines of code)
156 very simple units (590 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def train() in experiments/train.py	85	39	1
def __call__() in maddpg/common/tf_util.py	24	8	3
def update() in maddpg/trainer/maddpg.py	28	6	3
def __init__() in maddpg/common/tf_util.py	10	5	6
def make_pdtype() in maddpg/common/distributions.py	13	5	1
def minimize_and_clip() in maddpg/common/tf_util.py	9	4	4
def p_train() in maddpg/trainer/maddpg.py	29	4	11
def q_train() in maddpg/trainer/maddpg.py	22	4	10
def ensure_tf_input() in maddpg/common/tf_util.py	7	3	1
def scope_vars() in maddpg/common/tf_util.py	5	3	2
def function() in maddpg/common/tf_util.py	9	3	4
def _feed_input() in maddpg/common/tf_util.py	5	3	4
def get_trainers() in experiments/train.py	13	3	4
def sum() in maddpg/common/tf_util.py	2	2	3
def mean() in maddpg/common/tf_util.py	2	2	3
def max() in maddpg/common/tf_util.py	2	2	3
def min() in maddpg/common/tf_util.py	2	2	3
def is_placeholder() in maddpg/common/tf_util.py	2	2	1
def load_state() in maddpg/common/tf_util.py	5	2	2
def save_state() in maddpg/common/tf_util.py	6	2	2
def mode() in maddpg/common/distributions.py	2	2	1
def logp() in maddpg/common/distributions.py	2	2	2
def kl() in maddpg/common/distributions.py	4	2	2
def entropy() in maddpg/common/distributions.py	2	2	1
def sample() in maddpg/common/distributions.py	2	2	1
def mode() in maddpg/common/distributions.py	5	2	1
def logp() in maddpg/common/distributions.py	2	2	2
def kl() in maddpg/common/distributions.py	4	2	2
def entropy() in maddpg/common/distributions.py	2	2	1
def sample() in maddpg/common/distributions.py	5	2	1
def shape_el() in maddpg/common/distributions.py	6	2	2
def add() in maddpg/trainer/replay_buffer.py	7	2	6
def _encode_sample() in maddpg/trainer/replay_buffer.py	11	2	2
def make_index() in maddpg/trainer/replay_buffer.py	2	2	2
def make_latest_index() in maddpg/trainer/replay_buffer.py	4	2	2
def sample() in maddpg/trainer/replay_buffer.py	6	2	2
def discount_with_dones() in maddpg/trainer/maddpg.py	8	2	3
def make_update_exp() in maddpg/trainer/maddpg.py	7	2	2
def __init__() in maddpg/trainer/maddpg.py	34	2	8
def mlp_model() in experiments/train.py	7	2	6
def make_env() in experiments/train.py	10	2	3
def var() in maddpg/common/tf_util.py	3	1	3
def std() in maddpg/common/tf_util.py	2	1	3
def concatenate() in maddpg/common/tf_util.py	2	1	2
def argmax() in maddpg/common/tf_util.py	2	1	2
def softmax() in maddpg/common/tf_util.py	2	1	2
def __init__() in maddpg/common/tf_util.py	2	1	2
def get() in maddpg/common/tf_util.py	2	1	1
def make_feed_dict() in maddpg/common/tf_util.py	2	1	1
def __init__() in maddpg/common/tf_util.py	3	1	2