pengzhenghao

#!/usr/bin/env bash
arr=(1 10 100)
for d in ${arr[*]}
do
echo $d
echo
done

pengzhenghao

from collections import deque

class FPSTimer(object):
    def __init__(self, log_interval=10):
        self.cnt = 0
        self.log_interval = log_interval
        self.queue = deque(maxlen=log_interval)
        self.queue.append(time.time())

pengzhenghao

index = input("You are giving a exp_dir which supposed to contain only one trial, "
              "but we found many. \nPlease input the index of the trial you want to "
              "visualize:\n\n{}\nYour index:".format(
"".join(["{}: {}\n".format(i, t) for i, t in enumerate(potential_trial_paths)])))

"""The output look as following, it's quite clean.
You are giving a exp_dir which supposed to contain only one trial, but we found many. 
Please input the index of the trial you want to visualize:

0: /Users/pengzhenghao/ray_results/debug/PPO_tollgate_0_2019-04-09_21-10-02k6f8lt62

pengzhenghao

from collections import deque
class MovingAverage(object):
    def __init__(self, max_len=10):
        self.val = deque(maxlen=max_len)
        self.avg = 0
        self.maxlen = max_len

    def update(self, val):
        self.val.append(val)
        self.avg = sum(self.val) / self.maxlen

pengzhenghao

from gym.envs.box2d.bipedal_walker import (
    BipedalWalker, VIEWPORT_H, VIEWPORT_W, SCALE, TERRAIN_HEIGHT, TERRAIN_STEP
)
from Box2D.b2 import circleShape

import cv2
import numpy as np
import copy
import uuid

pengzhenghao

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# agents_name = ["ours", "DDPG"]
x_name = "uniqueness"
y_name = "performance"
dpi = 300
figsize = (12, 8)

pengzhenghao

import IPython
import tempfile
import PIL

def animate(img_array):
    path = tempfile.mkstemp(suffix=".gif")[1]
    images = [PIL.Image.fromarray(frame) for frame in img_array]
    images[0].save(
        path,
        save_all=True,

pengzhenghao

from toolbox.process_data import parse

data3 = []

a2c_data3 = parse("/home/zhpeng/OLD-novel-rl-archive-20200210/0206-a2c-baseline-large-Walker2d-v3-Walker2d-v3-50000000ts.pkl")
a2c_data3["label"] = "A2C"
a2c_data3.episode_reward_mean *= 5
data3.append(a2c_data3)

ppo_data3 = parse("/home/zhpeng/0119-ppo-baseline-walker-Walker2d-v3-50000000ts.pkl")

pengzhenghao

import json
import numpy as np

class NumpyEncoder(json.JSONEncoder):
    """ Special json encoder for numpy types """

    def default(self, obj):
        if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,
                            np.int16, np.int32, np.int64, np.uint8,

pengzhenghao

x = "timesteps_total"
y = "novelty_reward_mean"

ppo_path = "/home/sunhao/ray_results/0131-dece-use_bisector-use_bisector"

clip = None

dece_path = "/home/sunhao/ray_results/0102-dece"
dece_df = parse(dece_path,  
                ["seed", "constrain_novelty", "delay_update", "replay_values", "use_diversity_value_network"],