araffin

import gym 

from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines import PPO2

env = DummyVecEnv([lambda: gym.make("Reacher-v2")])
# Automatically normalize the input features
env = VecNormalize(env, norm_obs=True, norm_reward=False,
                   clip_obs=10.)

araffin

from stable_baselines.common.cmd_util import make_atari_env
from stable_baselines.common.policies import CnnPolicy
from stable_baselines import PPO2

# There already exists an environment generator
# that will make and wrap atari environments correctly
env = make_atari_env('DemonAttackNoFrameskip-v4', num_env=8, seed=0)

model = PPO2(CnnPolicy, env, verbose=1)
model.learn(total_timesteps=10000)

araffin

import imageio
import numpy as np

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

model = A2C(MlpPolicy, "LunarLander-v2").learn(100000)

images = []                                                                                              
obs = model.env.reset()

araffin

from stable_baselines.common.cmd_util import make_atari_env
from stable_baselines.common.policies import CnnPolicy
from stable_baselines.common.vec_env import VecFrameStack
from stable_baselines import ACER

# There already exists an environment generator
# that will make and wrap atari environments correctly.
# Here we are also multiprocessing training (num_env=4 => 4 processes)
env = make_atari_env('PongNoFrameskip-v4', num_env=4, seed=0)
# Frame-stacking with 4 frames

araffin

import os

import gym
import numpy as np
import matplotlib.pyplot as plt

from stable_baselines.common.vec_env.dummy_vec_env import DummyVecEnv
from stable_baselines.bench import Monitor
from stable_baselines.results_plotter import load_results, ts2xy
from stable_baselines import DDPG

araffin

from stable_baselines import PPO2

# Define and train a model in one line of code !
trained_model = PPO2('MlpPolicy', 'CartPole-v1').learn(total_timesteps=10000)
# you can then access the gym env using trained_model.get_env()

araffin

from stable_baselines.common.policies import FeedForwardPolicy
from stable_baselines import A2C

# Custom MLP policy of three layers of size 128 each
class CustomPolicy(FeedForwardPolicy):
    def __init__(self, *args, **kwargs):
        super(CustomPolicy, self).__init__(*args, **kwargs,
                                           layers=[128, 128, 128],
                                           feature_extraction="mlp")

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):
    """

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

# 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