def train(alg, task):
if task == 'reach':
env_fn = lambda: SawyerReachEnv(n_substeps=25, reward_type='dense')
elif task == 'grasp':
env_fn = lambda: SawyerGraspEnv(n_substeps=5, reward_type='dense')
ac_kwargs = dict(hidden_sizes=[64, 64], activation=tf.nn.relu)
save_path = os.path.join(SAVE_PATH, task, alg)
if alg == 'ppo':
# mpi_fork(2)
logger_kwargs = dict(output_dir=save_path, exp_name=EXP_NAME)
ppo(env_fn=env_fn,
steps_per_epoch=4000,
epochs=20000,
logger_kwargs=logger_kwargs,
max_ep_len=1000)
elif alg == 'ddpg':
logger_kwargs = dict(output_dir=SAVE_PATH + '/ddpg_suite',
exp_name=EXP_NAME)
ddpg(env_fn=env_fn,
steps_per_epoch=5000,
batch_size=256,
epochs=2000,
logger_kwargs=logger_kwargs,
max_ep_len=200)
elif alg == 'trpo':
logger_kwargs = dict(output_dir=SAVE_PATH + '/trpo_suite',
exp_name=EXP_NAME)
trpo(env_fn=env_fn,
ac_kwargs=ac_kwargs,
steps_per_epoch=5000,
epochs=2000,
logger_kwargs=logger_kwargs,
max_ep_len=200)
elif alg == 'td3':
logger_kwargs = dict(output_dir=save_path, exp_name=EXP_NAME)
td3(env_fn=env_fn,
start_steps=100000,
steps_per_epoch=5000,
epochs=2000,
logger_kwargs=logger_kwargs,
max_ep_len=1000)
elif alg == 'sac':
logger_kwargs = dict(output_dir=save_path, exp_name=EXP_NAME)
sac(env_fn=env_fn,
start_steps=100000,
steps_per_epoch=5000,
epochs=2000,
logger_kwargs=logger_kwargs,
max_ep_len=200)
def test_cartpole(self):
"""
Test training a small agent in a simple environment
"""
env_fn = partial(gym.make, 'CartPole-v1')
ac_kwargs = dict(hidden_sizes=(32, ))
with tf.Graph().as_default():
ppo(env_fn, steps_per_epoch=100, epochs=10, ac_kwargs=ac_kwargs)
def main():
import tensorflow as tf
env_fn = lambda: Doubling()
ac_kwargs = dict(hidden_sizes=[50, 50], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='output_dir3',
exp_name='training_64x64relu')
ppo(env_fn=env_fn,
ac_kwargs=ac_kwargs,
steps_per_epoch=5000,
epochs=25000000000,
logger_kwargs=logger_kwargs,
save_freq=1)
def __call__(self, *args, **kwargs):
ac_kwargs = dict(hidden_sizes=[400, 300, 200, 100],
activation=torch.nn.ReLU)
logger_kwargs = dict(output_dir=self.outdir, exp_name=self.expt_name)
ppo(
env_fn=self.env,
ac_kwargs=ac_kwargs,
# steps_per_epoch=250,
steps_per_epoch=1000,
epochs=400,
logger_kwargs=logger_kwargs)
def main():
args = {
"forest_data_path":
"/Users/anmartin/Projects/summer_project/hl_planner/forest_data.tiff",
"num_measurements": 6,
"max_forest_heights": [60, 90, 45, 38, 30, 76],
"orbit_altitude": 757000,
}
env_fn = lambda: gym.make('gym_orekit:online-orekit-v0', **args)
ac_kwargs = dict(hidden_sizes=[64, 64], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='./output', exp_name='test1')
ppo(env_fn=env_fn,
ac_kwargs=ac_kwargs,
steps_per_epoch=20160,
epochs=10,
max_ep_len=20160,
save_freq=2,
logger_kwargs=logger_kwargs)
def main():
env_fn = lambda : gym.make('LunarLander-v2')
ac_kwargs = dict(hidden_sizes=[64,64], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='path/to/output_dir', exp_name='experiment_name')
ppo(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=250, logger_kwargs=logger_kwargs)
# pass
######------play movie -----------------
# movie = retro.Movie('SuperMarioBros-Nes-Level1-1-000000.bk2')
# movie.step()
# env = retro.make(
# game=movie.get_game(),
# state=None,
# # bk2s can contain any button presses, so allow everything
# use_restricted_actions=retro.Actions.ALL,
# players=movie.players,
# )
# env.initial_state = movie.get_state()
# env.reset()
# while movie.step():
# keys = []
# for p in range(movie.players):
# for i in range(env.num_buttons):
# keys.append(movie.get_key(i, p))
# env.step(keys)
# env.render()
#########---------main RL program--------------
env=retro.make(game='SuperMarioBros-Nes',record='.')
obv=env.reset()
for i in range(10000):
obs,rew,done,info=env.step(env.action_space.sample())
env.render()
env.close()
def run_tests(model_name=None):
global epochs
global env
global ac
env = gym.make('StrategyEnv-v0')
test_tf = '20200201-20200207'
# test_tf = '20200401-'
env.set_timeframe(test_tf)
env.full_reset()
def make_env():
env = gym.make('StrategyEnv-v0')
env.set_timeframe('20191110-20200131')
env.randomize_timeframe(True)
env.set_ac(True)
env.full_reset()
return env
#ask joey to run normal BT to compare
run_test_with_strat(env)
env.run_normal_bt()
if model_name:
torch.manual_seed(10000)
np.random.seed(10000)
ac = models.load_model(model_name, env.observation_space,
env.action_space)
else:
# ac = ppo(make_env, epochs=epochs, target_kl=0.001, steps_per_epoch=7200, max_ep_len=100000)
ac = ppo(make_env,
epochs=epochs,
steps_per_epoch=7200,
max_ep_len=100000)
model_name = models.save_model(ac)
run_model_test(env, ac, model_name)
from spinup import ppo_pytorch as ppo
from spinup.exercises.common import print_result
from functools import partial
import gym
import os
import pandas as pd
import psutil
import time
logdir = "/tmp/experiments/%i" % int(time.time())
ActorCritic = partial(exercise1_2_auxiliary.ExerciseActorCritic,
actor=MLPGaussianActor)
ppo(env_fn=lambda: gym.make('MountainCarContinuous-v0'),
actor_critic=ActorCritic,
ac_kwargs=dict(hidden_sizes=(64, )),
steps_per_epoch=4000,
epochs=20,
logger_kwargs=dict(output_dir=logdir))
# Get scores from last five epochs to evaluate success.
data = pd.read_table(os.path.join(logdir, 'progress.txt'))
last_scores = data['AverageEpRet'][-5:]
# Your implementation is probably correct if the agent has a score >500,
# or if it reaches the top possible score of 1000, in the last five epochs.
correct = np.mean(last_scores) > 500 or np.max(last_scores) == 1e3
print_result(correct)
lam=float(lam))
# train with PPO
if algorithm == 'ppo':
clip_ratio = sys.argv[2]
target_kl = sys.argv[3]
exp_name = 'll_ppo_seed' + str(seed) + '_epochs' + str(epochs)
exp_name += '_cr' + clip_ratio + '_tk' + target_kl
logger_kwargs = dict(output_dir='data_spinning_up/' + exp_name + '/',
exp_name=exp_name)
ppo(env_fn=env_fn,
ac_kwargs=ac_kwargs,
max_ep_len=1000,
gamma=0.99,
seed=seed,
steps_per_epoch=steps_per_epoch,
pi_lr=0.005,
vf_lr=0.005,
epochs=epochs,
logger_kwargs=logger_kwargs,
clip_ratio=float(clip_ratio),
target_kl=float(target_kl))
# train with TRPO
if algorithm == 'trpo':
delta = sys.argv[2]
backtrack_coef = sys.argv[3]
exp_name = 'll_trpo_seed' + str(seed) + '_epochs' + str(epochs)
exp_name += '_delta' + delta + '_bc' + backtrack_coef
logger_kwargs = dict(output_dir='data_spinning_up/' + exp_name + '/',
exp_name=exp_name)
trpo(env_fn=env_fn,
from spinup import ppo_tf1 as ppo
import tensorflow as tf
import gym
env_fn = lambda: gym.make('CartPole-v0')
ac_kwargs = dict(hidden_sizes=[8, 16, 8], activation=tf.nn.relu)
ppo(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=300, epochs=250)
"""
Run this file to verify your solution.
"""
from spinup import ppo
from spinup.exercises.common import print_result
import gym
import os
import pandas as pd
import psutil
import time
import pybullet_envs
logdir = "/tmp/experiments/%i"%int(time.time())
tf_hidden_sizes = (64,)
keras_hidden_sizes = (64,64)
ppo(env_fn = lambda : gym.make('InvertedPendulumBulletEnv-v0'),
ac_kwargs=dict(policy=mlp_gaussian_policy, hidden_sizes=keras_hidden_sizes),
steps_per_epoch=4000, epochs=20, logger_kwargs=dict(output_dir=logdir))
# Get scores from last five epochs to evaluate success.
data = pd.read_table(os.path.join(logdir,'progress.txt'))
last_scores = data['AverageEpRet'][-5:]
# Your implementation is probably correct if the agent has a score >500,
# or if it reaches the top possible score of 1000, in the last five epochs.
correct = np.mean(last_scores) > 500 or np.max(last_scores)==1e3
print_result(correct)
def test_cartpole(self):
''' Test training a small agent in a simple environment '''
env_fn = partial(gym.make, 'CartPole-v1')
ac_kwargs = dict(hidden_sizes=(32,))
ppo(env_fn, steps_per_epoch=100, epochs=10, ac_kwargs=ac_kwargs)
noise=noise)
dirOut = dirO + 'TFIM' + "P" + str(Nt) + '_N' + str(
Ns) + '_rw' + rtype
elif model == 'RandomTFIM':
J_couplings = set_couplings(Ns, seed)
env_fn = lambda: qenv.RandomTFIM(Ns,
J_couplings,
Nt,
rtype,
dt,
actType,
measured_obs=measured_obs,
g_target=hfield,
noise=noise)
dirOut = dirO + 'RandomIsing' + "P" + str(Nt) + '_N' + str(
Ns) + '_rw' + rtype
else:
raise ValueError(f'Invalid model:{model}')
dirOut += '/' + measured_obs + '/network' + str(layers[0]) + 'x' + str(
layers[1])
ac_kwargs = dict(hidden_sizes=layers, activation=tf.nn.relu)
logger_kwargs = dict(output_dir=dirOut, exp_name='RL_first_try')
ppo(env_fn=env_fn,
ac_kwargs=ac_kwargs,
steps_per_epoch=nstep,
epochs=epochs,
logger_kwargs=logger_kwargs,
gamma=1.0,
target_kl=0.01,
save_freq=128)
from spinup import ppo_pytorch as ppo
from spinup.exercises.common import print_result
from functools import partial
from spinup.utils.run_utils import set_mujoco
set_mujoco()
import gym
import os
import pandas as pd
import psutil
import time
logdir = "/tmp/experiments/%i" % int(time.time())
ActorCritic = partial(exercise1_2_auxiliary.ExerciseActorCritic,
actor=MLPGaussianActor)
ppo(env_fn=lambda: gym.make('InvertedPendulum-v2'),
actor_critic=ActorCritic,
ac_kwargs=dict(hidden_sizes=(64, )),
steps_per_epoch=4000,
epochs=20,
logger_kwargs=dict(output_dir=logdir))
# Get scores from last five epochs to evaluate success.
data = pd.read_table(os.path.join(logdir, 'progress.txt'))
last_scores = data['AverageEpRet'][-5:]
# Your implementation is probably correct if the agent has a score >500,
# or if it reaches the top possible score of 1000, in the last five epochs.
correct = np.mean(last_scores) > 500 or np.max(last_scores) == 1e3
print_result(correct)
from spinup import ppo_tf1 as ppo
import tensorflow as tf
import gym
from gym.wrappers import FlattenObservation
import panda_gym
EPOCHS = 100
STEPS_PER_EPOCH = 4000
ENV = 'PandaPush-v1'
def env_fn():
env = gym.make(ENV)
print(env.observation_space)
env = FlattenObservation(env)
return env
ac_kwargs = dict(
hidden_sizes=[64, 64],
activation=tf.nn.sigmoid,
)
logger_kwargs = dict(output_dir=f'logs/sigmoid_{ENV}',
exp_name=f'exp_sigmoid_{ENV}')
ppo(env_fn=env_fn,
ac_kwargs=ac_kwargs,
steps_per_epoch=STEPS_PER_EPOCH,
epochs=EPOCHS,
logger_kwargs=logger_kwargs)
#vpg
# spinup.vpg(
# env,
# ac_kwargs={"hidden_sizes":(64,2)},
# seed = np.random.randint(100),
# steps_per_epoch=1250,
# epochs=2500,
# pi_lr=3e-4,
# logger_kwargs = {"output_dir" : "logs/vpgrandomtest"}
# )
#ppo
spinup.ppo(env,
ac_kwargs={"hidden_sizes": (64, 2)},
seed=np.random.randint(100),
steps_per_epoch=1250,
pi_lr=3e-4,
epochs=2500,
logger_kwargs={"output_dir": "logs/ppo-v3-0-rerun2"})
#polynomials
# spinup.vpgpolynomial(
# env,
# ac_kwargs={"order":3},
# seed = np.random.randint(100),
# steps_per_epoch=1250,
# epochs=2500,
# pi_lr=2e-5,
# l1_scaling=0.001,
# logger_kwargs = {"output_dir" : "logs/polyrandomtest"}
# )
from spinup import ppo_pytorch as ppo
from spinup import ddpg_pytorch as ddpg
from spinup import sac_pytorch as sac
import tensorflow as tf
import gym
import torch
"""
env_fn = lambda : gym.make('Walker2d-v2')
ac_kwargs = dict(hidden_sizes=[64,64])
logger_kwargs = dict(output_dir='baseline_data/walker/ppo', exp_name='walker_ppo')
ppo(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=200, logger_kwargs=logger_kwargs)
#env_fn = lambda : gym.make('Walker2d-v2')
#ac_kwargs = dict(hidden_sizes=[64,64], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='baseline_data/walker/ddpg', exp_name='walker_ddpg')
ddpg(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=200, logger_kwargs=logger_kwargs)
logger_kwargs = dict(output_dir='baseline_data/walker/sac', exp_name='walker_sac')
sac(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=200, logger_kwargs=logger_kwargs)
env_fn = lambda : gym.make('Hopper-v2')
logger_kwargs = dict(output_dir='baseline_data/hopper/ppo', exp_name='hopper_ppo')
ppo(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=200, logger_kwargs=logger_kwargs)
# env_fn = lambda : gym.make('Walker2d-v2')
# ac_kwargs = dict(hidden_sizes=[64,64], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='baseline_data/hopper/ddpg', exp_name='hopper_ddpg')
ddpg(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=200, logger_kwargs=logger_kwargs)
logger_kwargs = dict(output_dir='baseline_data/hopper/sac', exp_name='hopper_sac')
sac(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=200, logger_kwargs=logger_kwargs)
# Check this, it may not work
def envFunc():
env = gym.make('airsim_gym-v0')
return env
# Setup the environment function and hyperparameters
env_fn = envFunc
ac_kwargs = dict(hidden_sizes=[64, 64])
logger_kwargs = dict(
output_dir='/home/isra/Documents/airsim_exp_results_random_seed_0',
exp_name='random_goals')
ppo(env_fn=envFunc,
ac_kwargs=ac_kwargs,
seed=0,
max_ep_len=500,
steps_per_epoch=4000,
epochs=250,
logger_kwargs=logger_kwargs)
logger_kwargs = dict(
output_dir='/home/isra/Documents/airsim_exp_results_random_seed_5',
exp_name='random_goals')
ppo(env_fn=envFunc,
ac_kwargs=ac_kwargs,
seed=5,
max_ep_len=500,
steps_per_epoch=4000,
epochs=250,
logger_kwargs=logger_kwargs)
logger_kwargs = dict(
output_dir='/home/isra/Documents/airsim_exp_results_random_seed_10',
"""
from spinup import ppo
from spinup.exercises.common import print_result
from spinup.user_config import INVERTEDPENDULUM_ENV, IMPORT_USER_MODULES
import gym
import os
import pandas as pd
import psutil
import time
import importlib
for module in IMPORT_USER_MODULES:
importlib.import_module(module)
logdir = "/tmp/experiments/%i" % int(time.time())
ppo(env_fn=lambda: gym.make(INVERTEDPENDULUM_ENV),
ac_kwargs=dict(policy=mlp_gaussian_policy, hidden_sizes=(64, )),
steps_per_epoch=4000,
epochs=20,
logger_kwargs=dict(output_dir=logdir))
# Get scores from last five epochs to evaluate success.
data = pd.read_table(os.path.join(logdir, 'progress.txt'))
last_scores = data['AverageEpRet'][-5:]
# Your implementation is probably correct if the agent has a score >500,
# or if it reaches the top possible score of 1000, in the last five epochs.
correct = np.mean(last_scores) > 500 or np.max(last_scores) == 1e3
print_result(correct)
import gym
import gym_geofriend2
from MapGenerators.Basic import Basic
from MapGenerators.Pyramid import Pyramid
from MapGenerators.HighPlatform import HighPlatform
from MapGenerators.TwoHighTowers import TwoHighTowers
from Player.Player import Player
from spinup import ppo
import tensorflow as tf
env_fn = lambda : gym.make("geofriend2-v0")
# ac_kwargs = dict(hidden_sizes=[64,64], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='spinupPpo', exp_name='experiment')
ppo(env_fn=env_fn, steps_per_epoch=5000, epochs=500, logger_kwargs=logger_kwargs)
from spinup.utils.run_utils import ExperimentGrid
from spinup import ppo
import tensorflow as tf
import gym
# todo: define env_fn in terms of some gym environment
env_fn = lambda: gym.make('LunarLanderContinuous-v2')
ac_kwargs = dict(hidden_sizes=[64, 64], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='data/test1', exp_name='test1')
ppo(env_fn=env_fn,
ac_kwargs=ac_kwargs,
steps_per_epoch=5000,
max_ep_len=1000,
epochs=500,
logger_kwargs=logger_kwargs)
import gym
import gym_env
from spinup import ppo_pytorch as ppo
from gym_env.wrapper import PendulumCostWrapper
env = gym.make('Pendulum-v0')
env._max_episode_steps = 100
env = PendulumCostWrapper(env)
ppo(env_fn=lambda: env,
ac_kwargs=dict(hidden_sizes=[16] * 2),
gamma=0.99,
max_ep_len=1000,
lam=0.95,
epochs=100000,
seed=1)
import torch
import gym
import numpy as np
from spinup import ppo_pytorch as ppo
def env_fn():
import vortex_cartpole
# We can pass a dictionary of arguments to the environment using kwargs
# headless : True or False, selects whether or not to use graphics rendering
# random_reset : if True, a random state is induced when the environment is reset
#
kwargs = {"headless": False, "random_reset": True}
env = gym.make('VortexCartPole-v0', **kwargs)
return env
# Test training am agent using pytorch PPO
ac_kwargs = dict(hidden_sizes=[32, 32], activation=torch.nn.ReLU)
ppo(env_fn,
steps_per_epoch=1000,
epochs=50,
gamma=0.99,
pi_lr=1e-3,
vf_lr=1e-3,
ac_kwargs=ac_kwargs)
def test_atari_env(self):
''' Test training a small agent in a simple environment '''
env_fn = partial(gym.make, 'CarRacing-v0')
ac_kwargs = dict(hidden_sizes=(32, ))
with tf.Graph().as_default():
ppo(env_fn, steps_per_epoch=100, epochs=10, ac_kwargs=ac_kwargs)
return pi, logp, logp_pi
if __name__ == '__main__':
"""
Run this file to verify your solution.
"""
from spinup import ppo_tf1 as ppo
from spinup.exercises.common import print_result
import gym
import os
import pandas as pd
import psutil
import time
logdir = "/tmp/experiments/%i" % int(time.time())
ppo(env_fn=lambda: gym.make('InvertedPendulum-v2'),
ac_kwargs=dict(policy=mlp_gaussian_policy, hidden_sizes=(64, )),
steps_per_epoch=4000,
epochs=1,
logger_kwargs=dict(output_dir=logdir))
# Get scores from last five epochs to evaluate success.
data = pd.read_table(os.path.join(logdir, 'progress.txt'))
last_scores = data['AverageEpRet'][-5:]
# Your implementation is probably correct if the agent has a score >500,
# or if it reaches the top possible score of 1000, in the last five epochs.
correct = np.mean(last_scores) > 500 or np.max(last_scores) == 1e3
print_result(correct)
def ppo_test():
# ac_kwargs = dict(hidden_sizes=[64,64])
# logger_kwargs = dict(output_dir='path/to/output_dir', exp_name='experiment_name')
ppo(env_fn=env, steps_per_epoch=50, epochs=10)
#vpg
# spinup.vpg(
# env,
# ac_kwargs={"hidden_sizes":(64,2)},
# seed = np.random.randint(100),
# steps_per_epoch=1250,
# epochs=2500,
# pi_lr=3e-4,
# logger_kwargs = {"output_dir" : "logs/vpgrandomtest"}
# )
#ppo
spinup.ppo(env,
ac_kwargs={"hidden_sizes": (64, 2)},
seed=np.random.randint(100),
steps_per_epoch=1250,
pi_lr=3e-3,
epochs=2500,
logger_kwargs={"output_dir": "logs/ppo-dptest-uscaling1-lr3e3"})
#polynomials
# spinup.vpgpolynomial(
# env,
# ac_kwargs={"order":3},
# seed = np.random.randint(100),
# steps_per_epoch=1250,
# epochs=2500,
# pi_lr=2e-5,
# l1_scaling=0.001,
# logger_kwargs = {"output_dir" : "logs/polyrandomtest"}
# )
running = not done
count += 1
if count > 100:
break
save_gif(color_images, path=color_output)
save_gif(object_images, path=object_output)
print("____________________________")
print("Target: {}".format(env.target))
print("Reward: {}".format(reward))
print("____________________________")
def save_gif(images, path="example.gif"):
with imageio.get_writer(path, mode='I') as writer:
for image in images:
writer.append_data(image)
if __name__ == '__main__':
"""
Run the code to verify the solution
"""
if to_train:
logdir = "data/experiments/%i"%int(time.time())
ppo(env_fn = GoalGridWorld,
actor_critic=mlp_actor_critic,
steps_per_epoch=100000, epochs=100, logger_kwargs=dict(output_dir=logdir))
else:
logdir = "data/experiments/%i/simple_save/"%int(exp_id)
simulate(path=logdir)
from spinup import ppo
import tensorflow as tf
import gym
import paint_svg
from paint_svg.algos.ppo.ppo import ppo
env_fn = lambda : gym.make('PaintSvg-v0')
ac_kwargs = dict(hidden_sizes=[64,64], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='test', exp_name='paint_svg')
ppo(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=250, logger_kwargs=logger_kwargs)
# env = gym.make('PaintSvg-v0')
# env.reset()
super(MoveTowardZ, self).__init__(env)
def action(self, action):
action[2] = -.3
return action
env = gym.make('panda-v0')
env = ProcessFrame84(env)
env = ImageToPyTorch(env)
env = MoveTowardZ(env)
image = env.reset()
plt.figure()
plt.imshow(image.squeeze(), cmap='gray')
plt.title('Example extracted screen')
plt.show()
env_fn = lambda: env
ac_kwargs = dict(hidden_sizes=[18, 64, 64], activation=nn.ReLU)
logger_kwargs = dict(output_dir='spinup', exp_name='panda_ppo')
#ppo(env_fn=env_fn,actor_critic=core.CNNActorCritic, ac_kwargs=ac_kwargs, steps_per_epoch=5000, epochs=250, logger_kwargs=logger_kwargs)
ppo(env_fn=env_fn,
actor_critic=core.CNNActorCritic,
ac_kwargs=ac_kwargs,
steps_per_epoch=2,
epochs=1,
logger_kwargs=logger_kwargs)
