araffin

import gymnasium as gym
import numpy as np
from gymnasium.envs.mujoco.mujoco_env import MujocoEnv

# Env initialization
env = gym.make("Swimmer-v4", render_mode="human")
# Wrap to have reward statistics
env = gym.wrappers.RecordEpisodeStatistics(env)
mujoco_env = env.unwrapped
n_joints = 2

araffin

import gymnasium as gym
import numpy as np
from gymnasium.envs.mujoco.mujoco_env import MujocoEnv

# Env initialization
env = gym.make("HalfCheetah-v4", render_mode="human")
# Wrap to have reward statistics
env = gym.wrappers.RecordEpisodeStatistics(env)
mujoco_env = env.unwrapped
n_joints = 6

araffin

from __future__ import division

import cv2
import numpy as np
# Input Image
image = cv2.imread("my_image.jpg")
# Convert to HSV color space
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
# Define range of white color in HSV
lower_white = np.array([0, 0, 212])

araffin

# From https://github.com/araffin/python-arduino-serial
from robust_serial import Order, write_order, write_i8, write_i16
from robust_serial.utils import open_serial_port

# Open serial port with a baudrate of 9600 (bits/s)
serial_file = open_serial_port(baudrate=9600)

# Send the order "MOTOR", i.e. to change the speed of the car
# equivalent to write_i8(serial_file, Order.MOTOR.value)
write_order(serial_file, Order.MOTOR)

araffin

// https://github.com/datalogix/google-fonts-helper
// npm install google-fonts-helper
import { download } from 'google-fonts-helper'

const downloader = download('https://fonts.googleapis.com/css?family=Montserrat:400,700%7CRoboto:400,400italic,700%7CRoboto+Mono&display=swap', {
  base64: false,
  overwriting: false,
  outputDir: './',
  stylePath: 'fonts.css',
  fontsDir: 'fonts',

araffin

import gym

from stable_baselines import A2C
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv

# Create and wrap the environment
env = gym.make('LunarLander-v2')
env = DummyVecEnv([lambda: env])

araffin

import gym
import numpy as np
import cma
from collections import OrderedDict

from stable_baselines import A2C


def flatten(params):
    """

araffin

# from https://github.com/hill-a/stable-baselines
import gym

from stable_baselines.common.policies import MlpPolicy
from stable_baselines import PPO2

env = gym.make('CartPole-v1')

model = PPO2(MlpPolicy, env, verbose=1)
# Train the agent

araffin

import pytest
import numpy as np

from stable_baselines import A2C, ACER, ACKTR, DQN, DDPG, PPO1, PPO2, TRPO
from stable_baselines.common import set_global_seeds

MODEL_LIST_DISCRETE = [
    A2C,
    ACER,
    ACKTR,

araffin

import gym
import numpy as np

from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import SubprocVecEnv
from stable_baselines.common import set_global_seeds
from stable_baselines import ACKTR

def make_env(env_id, rank, seed=0):
    """