araffin

"""Optuna example that optimizes the hyperparameters of
a reinforcement learning agent using PPO implementation from Stable-Baselines3
on a Gymnasium environment.

This is a simplified version of what can be found in https://github.com/DLR-RM/rl-baselines3-zoo.

You can run this example as follows:
    $ python optimize_ppo.py

"""

araffin

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from gymnasium import spaces
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.vec_env import VecEnvWrapper

sns.set_theme()

araffin

"""
A simple GUI to collect human feedback.
It writes the rating to a file "gui_value.txt" next to the script.
The rating can be reset by removing or emptying the text file.

Nicegui is the only dependency.
If you use `uv` you can do `uv run feedback_gui.py`.

Author: Antonin Raffin (2024)
MIT License

araffin

import sbx
import shimmy
import stable_baselines3 as sb3
from dm_control import suite
from gymnasium.wrappers import FlattenObservation
from stable_baselines3.common.env_checker import check_env

# Available envs:
# suite._DOMAINS
# suite.dog.SUITE

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

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

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

from stable_baselines import A2C


def flatten(params):
    """

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

tensorboard --logdir /tmp/a2c_cartpole_tensorboard/