pengzhenghao

import argparse
import multiprocessing
import os

from metadrive.utils.waymo_utils.script.convert_waymo_to_metadrive import parse_data
from tqdm.auto import tqdm

REPO_ROOT = os.path.dirname(os.path.dirname(__file__))

DATA_FOLDER = os.path.join(REPO_ROOT, "trafficgen_v2", "data", "waymo")

pengzhenghao

import argparse
import logging
import pickle
from collections import deque

import numpy as np
import ray
from ray import tune
from ray.rllib.agents.es.es import ESTrainer, utils
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID

pengzhenghao

"""
#!/bin/bash

# Please copy this string to a file name "sbatch_template.sh".

# THIS FILE IS GENERATED BY AUTOMATION SCRIPT! PLEASE REFER TO ORIGINAL SCRIPT!
# THIS FILE IS A TEMPLATE AND IT SHOULD NOT BE DEPLOYED TO PRODUCTION!

#SBATCH --partition={{PARTITION_NAME}}
#SBATCH --job-name={{JOB_NAME}}

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"],

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

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 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

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

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

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