def train():
eg = ExperimentGrid(name=experiment_name)
eg.add('env_name', env_config['env_name'], '', False)
# eg.add('seed', 0)
eg.add(
'resume',
'/home/c2/src/tmp/spinningup/data/intersection_2_agents_lower_gamma_snapshot/intersection_2_agents_lower_gamma_s0_2020_03-12_12-07.37'
)
eg.add('reinitialize_optimizer_on_resume', False)
eg.add(
'pi_lr', 3e-5
) # doesn't seem to have an effect, but playing it safe and lowering learning rate since we're not restoring adam rates
eg.add(
'vf_lr', 1e-4
) # doesn't seem to have an effect, but playing it safe and lowering learning rate since we're not restoring adam rates
eg.add('epochs', 8000)
eg.add('gamma', 0.95)
eg.add('lam', 0.835)
# eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', (256, 256), 'hid')
eg.add('ac_kwargs:activation', torch.nn.Tanh, '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
eg.run(ppo_pytorch)
def train():
eg = ExperimentGrid(name=experiment_name)
eg.add('env_name', env_config['env_name'], '', False)
# eg.add('seed', 0)
eg.add('epochs', 8000)
# eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', (256, 256), 'hid')
eg.add('ac_kwargs:activation', torch.nn.Tanh, '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
eg.run(ppo_pytorch)
def runExperiment(someEnv,someOptimizer, someActivation):
env = ExperimentGrid(name='vpg-trece')
env.add('env_name', someEnv, '', True)
# eg.add('clip_ratio', [0.1,0.2])
env.add('seed', [10*i for i in range(args.num_runs)])
env.add('epochs', 10)
env.add('steps_per_epoch', [4000])
#someOptimizer should be list
env.add('optimizer',[someOptimizer])
env.add('ac_kwargs:hidden_sizes', [(32,), (64,64)], 'hid')
env.add('ac_kwargs:activation', [someActivation], '')
env.run(vpg, num_cpu=args.cpu)
def run_experiment(args):
# def env_fn():
# import flexibility # register flexibility to gym env registry
# return gym.make(args.env_name)
eg = ExperimentGrid(name=args.exp_name)
eg.add('seed', [10*i for i in range(args.num_runs)] if args.seed is None else args.seed)
eg.add('epochs', args.epochs)
eg.add('steps_per_epoch', args.steps_per_epoch)
eg.add('save_freq', args.save_freq)
eg.add('max_ep_len', 200)
eg.add('ac_kwargs:activation', eval(args.act), '')
eg.add('custom_h', args.custom_h)
eg.add('do_checkpoint_eval', args.do_checkpoint_eval)
eg.add('eval_episodes', args.eval_episodes)
eg.add('train_v_iters', args.train_v_iters)
eg.add('eval_temp', args.eval_temp)
eg.add('train_starting_temp', args.train_starting_temp)
eg.add('gamma', args.gamma)
eg.add('env_version', args.env_version)
eg.add('env_name', args.env_name)
eg.add('env_subtract_full_flex', args.env_subtract_full_flex)
eg.add('meta_learning', args.meta_learning)
eg.add('finetune', args.finetune)
eg.add('finetune_model_path', args.finetune_model_path)
eg.add('lam', args.lam)
eg.add('early_stop_epochs', args.early_stop_epochs)
eg.add('save_all_eval', args.save_all_eval)
if args.episodes_per_epoch is not None:
eg.add('episodes_per_epoch', args.episodes_per_epoch)
if args.env_version >= 3:
# args.file_path = "/home/user/git/spinningup/spinup/FlexibilityEnv/input_m8n12_cv0.8.pkl"
prefix = os.getcwd().split('RL_flex_design')[0]
args.file_path = prefix + "RL_flex_design/spinup/FlexibilityEnv_input/{}".format(args.env_input)
m, n, mean_c, mean_d, sd_d, profit_mat, target_arcs, fixed_costs, flex_0 = load_FlexibilityEnv_input(args.file_path)
eg.add('env_input', args.file_path)
eg.add('env_n_sample', args.env_n_sample)
if args.target_arcs is None:
eg.add('target_arcs', target_arcs)
else: # target_arcs is explicitly specified by the scripts, which overrides the target_arc from the input file
eg.add('target_arcs', args.target_arcs)
if args.algo == "ppo":
eg.add('train_pi_iters', args.train_pi_iters)
eg.run(ppo)
elif args.algo == "vpg":
eg.run(vpg)
def run_experiment(args):
def env_fn():
import HumanoidRL
return gym.make(args.env_name)
eg = ExperimentGrid(name=args.exp_name)
eg.add('env_fn', env_fn)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 500)
eg.add('steps_per_epoch', 10000)
eg.add('save_freq', 20)
eg.add('max_ep_len', 200)
eg.add('ac_kwargs:activation', tf.tanh, '')
eg.run(ppo_tf1)
def run_experiment(args):
def env_fn():
import offload_env # registers custom envs to gym env registry
env = gym.make('offload-v0')
return env
eg = ExperimentGrid(name=args.exp_name)
eg.add('env_fn', env_fn)
eg.add('seed', [10*i for i in range(args.num_runs)])
eg.add('epochs', 10)
eg.add('steps_per_epoch', 10000)
eg.add('save_freq', 20)
eg.add('ac_kwargs:hidden_sizes', [(32,), (64,64)], 'hid')
eg.add('ac_kwargs:activation', [tf.tanh, tf.nn.relu], '')
eg.run(vpg, num_cpu=args.cpu)
def run_vpg_lava():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--num_runs', type=int, default=3)
args = parser.parse_args()
eg = ExperimentGrid(name='vpg-pt-bench')
eg.add('env_name', 'MiniGrid-LavaCrossingS9N2-v1', '', True)
eg.add('seed', [6 * i for i in range(args.num_runs)])
eg.add('epochs', 500)
eg.add('steps_per_epoch', 5000)
eg.add('max_ep_len', 200)
# eg.add('ac_kwargs:hidden_sizes', [(32,), (64,64)], 'hid')
eg.add('ac_kwargs:hidden_sizes', [(32, )], 'hid')
# eg.add('ac_kwargs:activation', [torch.nn.Tanh, torch.nn.ReLU], '')
eg.add('ac_kwargs:activation', [torch.nn.Tanh], '')
eg.run(vpg_pytorch, num_cpu=args.cpu, datestamp=True)
def run_experiment(args, rl_model):
def env_fn():
import envs # registers custom envs to gym env registry
return gym.make(args.env_name, desired_outputs=args.desired_outputs)
eg = ExperimentGrid(name=args.exp_name)
eg.add('env_fn', env_fn)
# eg.add('seed', [10*i for i in range(args.num_runs)])
eg.add('epochs', 100)
eg.add('steps_per_epoch', 6*500) # FIXME
eg.add('save_freq', 10)
# eg.add('num_runs', args.num_runs)
eg.add('max_ep_len', 6) # FIXME get it from env
# ppo
eg.add('pi_lr', 3e-3)
# actor-critic
eg.add('ac_kwargs:activation', tf.tanh, '')
# eg.add('ac_kwargs:hidden_sizes', [32, 32])
eg.run(rl_model, num_cpu=args.cpu, data_dir=args.data_dir)
def train():
eg = ExperimentGrid(name=experiment_name)
eg.add('env_name', env_config['env_name'], '', False)
# eg.add('seed', 0)
eg.add(
'resume',
'/home/c2/src/tmp/spinningup/data/deepdrive-2d-intersection-no-constrained-controls-example/deepdrive-2d-intersection-no-constrained-controls-example_s0_2020_03-10_13-14.50/best_HorizonReturn/2020_03-11_11-36.27'
)
eg.add('reinitialize_optimizer_on_resume', False)
eg.add(
'pi_lr', 3e-5
) # doesn't seem to have an effect, but playing it safe and lowering learning rate since we're not restoring adam rates
eg.add(
'vf_lr', 1e-4
) # doesn't seem to have an effect, but playing it safe and lowering learning rate since we're not restoring adam rates
eg.add('epochs', 8000)
# eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', (256, 256), 'hid')
eg.add('ac_kwargs:activation', torch.nn.Tanh, '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
eg.run(ppo_pytorch)
from spinup.utils.run_utils import ExperimentGrid
env_fn = lambda: gym.make('Pendulum-v0')
network_kwargs = dict(hidden_sizes=[400, 300], activation=tf.nn.relu)
logger_kwargs = dict(output_dir='logging/NAF', exp_name='naf - tests')
steps_per_epoch = 1000
epochs = 100
start_steps = 50
algorithm = 'naf'
if __name__ == '__main__':
eg = ExperimentGrid(name='ddpg-bench-long')
eg.add('env_name', 'Pendulum-v0', '', True)
eg.add('seed', [10 * i for i in range(4)])
eg.add('epochs', 20)
eg.add('steps_per_epoch', 1000)
eg.add('ac_kwargs:hidden_sizes', [(100, 100), (400, 300)], 'hid')
eg.add('ac_kwargs:activation', [tf.nn.relu], '')
eg.run(ddpg, num_cpu=4, data_dir='logging/DDPG')
#
# # agent = naf(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=100, epochs=25, logger_kwargs=logger_kwargs)
#
# # agent = spinup.ddpg(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=500, epochs=250, logger_kwargs=logger_kwargs,
# # start_steps=start_steps)
# tf.reset_default_graph()
# naf(env_fn=env_fn, ac_kwargs=network_kwargs, steps_per_epoch=steps_per_epoch, epochs=epochs, logger_kwargs=logger_kwargs,
# act_noise=0.1, start_steps=start_steps)
from spinup.utils.run_utils import ExperimentGrid
from spinup import ppo_pytorch
import torch
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--num_runs', type=int, default=3)
args = parser.parse_args()
eg = ExperimentGrid(name='ppo-test-lunar')
#eg.add('env_name', 'MountainCar-v0', '', True)
#eg.add('env_name', 'CartPole-v0', '', True)
#eg.add('env_name', 'gym_multiagent_control:foo-v0', '', True)
eg.add('env_name', 'gym_multiagent_control:foo-v2', '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 10)
eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', [(32, ), (64, 64)], 'hid')
eg.add('ac_kwargs:activation', [torch.nn.Tanh, torch.nn.ReLU], '')
eg.run(ppo_pytorch, num_cpu=args.cpu)
# default to their own values. (Some are (64, 64) some are (300,400))
from spinup.utils.run_utils import ExperimentGrid
from spinup import vpg, trpo, ppo, ddpg, td3, sac
import tensorflow as tf
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
# This is set top auto and type str so that it can adapt to whatever algorithm im using
parser.add_argument('--cpu', type=str, default='auto')
parser.add_argument('--num_runs', type=int, default=1)
args = parser.parse_args()
algo_names = ['vpg', 'trpo', 'ppo', 'ddpg', 'td3', 'sac']
algo = [vpg, trpo, ppo, ddpg, td3, sac]
for i in range(len(algo)):
eg = ExperimentGrid(name=algo_names[i])
eg.add('env_name', 'MountainCarContinuous-v0', '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 10)
eg.add('steps_per_epoch', 4000)
# Use default hidden sizes in actor_critic function, comment below out
#eg.add('ac_kwargs:hidden_sizes', [(32,), (64,64)], 'hid')
eg.add('ac_kwargs:activation', [tf.nn.relu], '')
eg.run(algo[i]) #, num_cpu=args.cpu)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='HalfCheetah-v2')
parser.add_argument('--h', type=int, default=300)
parser.add_argument('--l', type=int, default=1)
parser.add_argument('--num_runs', '-n', type=int, default=3)
parser.add_argument('--steps_per_epoch', '-s', type=int, default=5000)
parser.add_argument('--total_steps', '-t', type=int, default=int(5e4))
args = parser.parse_args()
def ddpg_with_actor_critic(bugged, **kwargs):
actor_critic = bugged_mlp_actor_critic if bugged else mlp_actor_critic
return ddpg(actor_critic=actor_critic,
ac_kwargs=dict(hidden_sizes=[args.h] * args.l),
start_steps=5000,
max_ep_len=150,
batch_size=64,
polyak=0.95,
**kwargs)
eg = ExperimentGrid(name='ex2-2_ddpg')
eg.add('replay_size', int(args.total_steps))
eg.add('env_name', args.env, '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', int(args.total_steps / args.steps_per_epoch))
eg.add('steps_per_epoch', args.steps_per_epoch)
eg.add('bugged', [False, True])
eg.run(ddpg_with_actor_critic, datestamp=True)
logger.dump_tabular()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='HalfCheetah-v2')
parser.add_argument('--h', type=int, default=300)
parser.add_argument('--l', type=int, default=1)
parser.add_argument('--num_runs', '-n', type=int, default=3)
parser.add_argument('--steps_per_epoch', '-s', type=int, default=5000)
parser.add_argument('--total_steps', '-t', type=int, default=int(5e4))
args = parser.parse_args()
def td3_with_actor_critic(**kwargs):
td3(ac_kwargs=dict(hidden_sizes=[args.h] * args.l),
start_steps=5000,
max_ep_len=150,
batch_size=64,
polyak=0.95,
**kwargs)
eg = ExperimentGrid(name='ex2-3_td3')
eg.add('replay_size', int(args.total_steps))
eg.add('env_name', args.env, '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', int(args.total_steps / args.steps_per_epoch))
eg.add('steps_per_epoch', args.steps_per_epoch)
eg.add('remove_action_clip', [False, True])
eg.run(td3_with_actor_critic, datestamp=True)
from spinup import vpg
import tensorflow as tf
# ENVS
# BipedalWalkerHardcore-v2
# LunarLanderContinuous-v2
# MontezumaRevenge-ram-v0
# Enduro-ram-v0
# MsPacman-ram-v0
# Ant-v2
# HumanoidStandup-v2
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=1)
parser.add_argument('--num_runs', type=int, default=10)
# parser.add_argument('--clip_ratio', type=int, )
args = parser.parse_args()
Enduro = ExperimentGrid(name='vpg-nueve-singular')
Enduro.add('env_name', 'Enduro-ram-v0', '', True)
# eg.add('clip_ratio', [0.1,0.2])
Enduro.add('seed', [10*i for i in range(args.num_runs)])
Enduro.add('epochs', 10)
Enduro.add('steps_per_epoch', [4000,100])
Enduro.add('optimizer',['GradientDescentOptimizer', 'MomentumOptimizer', 'ProximalAdagradOptimizer', 'ProximalGradientDescentOptimizer', 'RMSPropOptimizer', 'AdaMaxOptimizer', 'AdamGSOptimizer', 'AdamWOptimizer', 'AddSignOptimizer', 'GGTOptimizer', 'LARSOptimizer', 'LazyAdamGSOptimizer', 'LazyAdamOptimizer', 'MomentumWOptimizer', 'NadamOptimizer', 'PowerSignOptimizer', 'RegAdagradOptimizer', 'ShampooOptimizer'])
Enduro.add('ac_kwargs:hidden_sizes', [(32,), (64,64)], 'hid')
Enduro.add('ac_kwargs:activation', [tf.nn.sigmoid], '')
Enduro.run(vpg, num_cpu=args.cpu)
#, 'ProximalAdagradOptimizer', 'ProximalGradientDescentOptimizer', 'RMSPropOptimizer', 'AdaMaxOptimizer', 'AdamGSOptimizer', 'AdamWOptimizer', 'AddSignOptimizer', 'GGTOptimizer', 'LARSOptimizer', 'LazyAdamGSOptimizer', 'LazyAdamOptimizer', 'MomentumWOptimizer', 'NadamOptimizer', 'PowerSignOptimizer', 'RegAdagradOptimizer', 'ShampooOptimizer'
# , , , , tf.nn.selu, tf.nn.softplus, tf.nn.softsign, tf.sigmoid, tf.tanh
remainder = remainder % division
indexes.append(index)
total = division
actual_setting = {}
for j in range(len(indexes)):
actual_setting[setting_names[j]] = settings[j][indexes[j]]
return indexes, actual_setting
indexes, actual_setting = get_setting(args.setting, total, settings,
setting_names)
eg = ExperimentGrid(name=EXPERIMENT_NAME)
# use eg.add to add parameters in the settings or add parameters tha apply to all jobs
# we now automated this part, as long as you added settings correctly into the arrays at the start of this program
# they should be added to experiment automatically
for i in range(len(actual_setting)):
setting_name = setting_names[i]
if setting_name != 'env_name' and setting_name != 'seed':
eg.add(setting_name, actual_setting[setting_name],
setting_savename_prefix[i], whether_add_to_savename[i])
eg.add('env_name', actual_setting['env_name'], '', True)
eg.add('seed', actual_setting['seed'])
eg.run(function_to_run, num_cpu=args.cpu, data_dir=save_data_dir)
print(
'\n###################################### GRID EXP END ######################################'
)
print('total time for grid experiment:', time.time() - start_time)
parser.add_argument('--num_runs', type=int, default=3)
args = parser.parse_args()
x = 10
layers = []
layers_itr = []
for i in range(x):
layers.append(64)
layers_itr.append(list(layers))
layers = []
for i in range(x):
layers.append(128)
layers_itr.append(list(layers))
layers = []
for i in range(x):
layers.append(256)
layers_itr.append(list(layers))
layers = []
for i in range(x):
layers.append(512)
layers_itr.append(list(layers))
eg = ExperimentGrid(name='td3-bench')
eg.add('env_name', 'MountainCarContinuous-v0', '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 10)
eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', layers_itr, 'hid')
eg.add('ac_kwargs:activation', [tf.nn.relu], '')
eg.run(td3, num_cpu=args.cpu)
# Enduro-ram-v0
# MsPacman-ram-v0
# Ant-v2
# HumanoidStandup-v2
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--num_runs', type=int, default=10)
# parser.add_argument('--clip_ratio', type=int, )
args = parser.parse_args()
LunarLanderContinuous = ExperimentGrid(name='vpg-ocho')
LunarLanderContinuous.add('env_name', 'LunarLanderContinuous-v2', '', True)
# eg.add('clip_ratio', [0.1,0.2])
LunarLanderContinuous.add('seed', [10 * i for i in range(args.num_runs)])
LunarLanderContinuous.add('epochs', 10)
LunarLanderContinuous.add('steps_per_epoch', [4000, 100])
LunarLanderContinuous.add('optimizer', [
'NadamOptimizer', 'PowerSignOptimizer', 'RegAdagradOptimizer',
'ShampooOptimizer'
])
LunarLanderContinuous.add('ac_kwargs:hidden_sizes', [(32, ), (64, 64)],
'hid')
LunarLanderContinuous.add('ac_kwargs:activation', [
tf.nn.relu, tf.nn.relu6, tf.nn.crelu, tf.nn.elu, tf.nn.selu,
tf.nn.softplus, tf.nn.softsign, tf.sigmoid, tf.tanh
], '')
LunarLanderContinuous.run(vpg, num_cpu=args.cpu)
division = int(total / len(setting))
index = int(remainder / division)
remainder = remainder % division
indexes.append(index)
total = division
actual_setting = {}
for j in range(len(indexes)):
actual_setting[setting_names[j]] = settings[j][indexes[j]]
return indexes, actual_setting
indexes, actual_setting = get_setting(args.setting, total, settings, setting_names)
####################################################################################################
## use eg.add to add parameters in the settings or add parameters that apply to all jobs
eg = ExperimentGrid(name=exp_name)
eg.add('ue_seed', 21, 'ues', True)
eg.add('lr', actual_setting['lr'], 'lr', True)
eg.add('border', actual_setting['border'], 'border', True)
eg.add('wd', 0, 'wd', True)
eg.add('buffer_type', 'FinalSigma0.5', 'Buf-', True)
eg.add('buffer_size', '500K', '', True)
eg.add('eval_freq', 500)
eg.add('max_timesteps', 100000)
eg.add('env_set', actual_setting['env_set'], '', True)
eg.add('seed', actual_setting['seed'])
eg.run(bc_ue_learn, num_cpu=args.cpu)
print('\n###################################### GRID EXP END ######################################')
print('total time for grid experiment:',time.time()-start_time)
eg.add('show_kwargs_json', True)
eg.add('env_name', 'Financial_gym_pic_daily', '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 400)
eg.add('save_freq', 5) # epoch save frequeece
eg.add('steps_per_epoch', 400) # default 4000
eg.add('start_steps', 500) # default 10000, start store a=pi(obs)
eg.add('update_after', 500) # default 1000, update
eg.add('use_gpu', True) # default
eg.add('gpu_parallel', True) # default
eg.add('update_times_every_step', 50) # default 50
eg.add('automatic_entropy_tuning', True) # default
eg.add('batch_size', 48) # default
eg.add('num_test_episodes', 2) # default
from spinup.algos.pytorch.sac.core import Discrete_Actor, Discrete_Critic, Xception_1
eg.add('state_of_art_model', True)
eg.add('Actor', [Discrete_Actor])
eg.add('Critic', [Discrete_Critic])
eg.add('ac_kwargs:model', [Xception_1])
eg.add('ac_kwargs:num_classes', [2])
# eg.add('ac_kwargs:hidden_sizes', [(2048, 1024, 512, 256)], 'hid')
# eg.add('ac_kwargs:activation', [torch.nn.ReLU], '')
eg.add(
"last_save_path",
"/media/zzw/Magic/py_work2019/RL/spinningup-master/data/sac-pyt_financial_gym_pic_daily/sac-pyt_financial_gym_pic_daily_s0/pyt_save/model.pt"
)
eg.run(fgym_trunk_sac_discrete_v2, num_cpu=args.cpu)
# Import packages and environment
import numpy as np
from spinup.utils.run_utils import ExperimentGrid
from spinup import soc_pytorch
# from spinup import sac_pytorch
# from spinup import ppo_pytorch
import torch as th
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--num_runs', type=int, default=3)
args = parser.parse_args()
eg = ExperimentGrid(name='hac-hopper-soc')
# eg.add('env_name', 'Walker2DBulletEnv-v0', '', True)
eg.add('env_name', 'HopperBulletEnv-v0', '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 250)
eg.add('N_options', [2]) # 2,3
eg.add('ac_kwargs:hidden_sizes', [[128, 256, 128]], 'hid')
eg.add('alpha', [0.1, 0.2])
eg.add('c', [0.2]) # 0.1,0.2,0.3 og evt. 0.0
eg.run(soc_pytorch)
if args.save_model_interval > 0 and (i_iter+1) % args.save_model_interval == 0:
to_device(torch.device('cpu'), policy_net, value_net)
pickle.dump((policy_net, value_net, running_state),
open(os.path.join(assets_dir(), 'learned_models/{}_ppo.p'.format(args.env_name)), 'wb'))
to_device(device, policy_net, value_net)
# """clean up gpu memory"""
torch.cuda.empty_cache()
return agent.evaluate()
print('a')
print(config)
print(args)
return main_loop(config)
def mock_train(**kwargs):
config = {
"lr": kwargs['lr'],
"gamma": kwargs['gamma']
}
print('a')
print(config)
print(args)
eg = ExperimentGrid('hopper')
eg.add('lr', [1e-4])
eg.add('gamma', [0.99, 0.95])
eg.run(train)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--num_runs', type=int, default=10)
# parser.add_argument('--clip_ratio', type=int, )
args = parser.parse_args()
MontezumaRevenge = ExperimentGrid(name='vpg-nueve-singular')
MontezumaRevenge.add('env_name', 'MontezumaRevenge-ram-v0', '', True)
# eg.add('clip_ratio', [0.1,0.2])
MontezumaRevenge.add('seed', [10 * i for i in range(args.num_runs)])
MontezumaRevenge.add('epochs', 10)
MontezumaRevenge.add('steps_per_epoch', [4000, 100])
MontezumaRevenge.add('optimizer', [
'GradientDescentOptimizer', 'MomentumOptimizer',
'ProximalAdagradOptimizer', 'ProximalGradientDescentOptimizer',
'RMSPropOptimizer', 'AdaMaxOptimizer', 'AdamGSOptimizer',
'AdamWOptimizer', 'AddSignOptimizer', 'GGTOptimizer', 'LARSOptimizer',
'LazyAdamGSOptimizer', 'LazyAdamOptimizer', 'MomentumWOptimizer',
'NadamOptimizer', 'PowerSignOptimizer', 'RegAdagradOptimizer',
'ShampooOptimizer'
])
MontezumaRevenge.add('ac_kwargs:hidden_sizes', [(32, ), (64, 64)], 'hid')
MontezumaRevenge.add('ac_kwargs:activation', [
tf.nn.relu, tf.nn.relu6, tf.nn.crelu, tf.nn.elu, tf.nn.selu,
tf.nn.softplus, tf.nn.softsign, tf.sigmoid, tf.tanh
], '')
MontezumaRevenge.run(vpg, num_cpu=args.cpu)
from spinup import vpg_pytorch
from spinup.utils.run_utils import ExperimentGrid
import torch
if __name__ == '__main__':
grid = ExperimentGrid(name='vpg-torch-cart-bench')
grid.add('env_name', 'CartPole-v0')
grid.add('seed', [0])
grid.add('epochs', 2)
grid.add('steps_per_epoch', 100)
grid.add('gamma', [0, 0.5, 1])
grid.add('ac_kwargs:hidden_sizes', [(32, ), (64, 64)], 'hid')
grid.add('ac_kwargs:activation', [torch.nn.Tanh], '')
grid.run(vpg_pytorch, num_cpu=4)
nargs='+',
default=(160, 160, 160, 160, 160, 160))
args = parser.parse_args()
hidden_sizes_name = '_'.join([str(num) for num in args.hidden_sizes])
#eg = ExperimentGrid(name='superpos_sac-MT10_with_bias_%s_context_q_%s' % (args.psp_type, hidden_sizes_name))
eg = ExperimentGrid(name='TIMETEST')
eg.add('env_name', 'MT10Helper-v0', '', True)
eg.add('num_tasks', 10)
eg.add('batch_size', 128) # This is per task, so real is 128 x 10
eg.add('psp_type', args.psp_type)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 1000)
eg.add('steps_per_epoch', TASK_HORIZON * PATHS_PER_TASK * NUM_TASKS)
eg.add('update_after', TASK_HORIZON * NUM_TASKS * PATHS_PER_TASK)
eg.add('lr', [3e-4])
eg.add('start_steps', TASK_HORIZON * PATHS_PER_TASK * NUM_TASKS)
#eg.add('update_every', NUM_TASKS * )
eg.add('num_test_episodes', 10 * NUM_TASKS)
eg.add('ac_kwargs:hidden_sizes', [tuple(args.hidden_sizes)], 'hid')
eg.add('ac_kwargs:activation', [torch.nn.ReLU], '')
eg.run(psp_sac_pytorch, num_cpu=args.cpu)
#from metaworld.benchmarks import MT10
#
#env_fn = lambda : MTEnv(MT10.get_train_tasks())
#
#ac_kwargs = dict(hidden_sizes=[400,400], activation=torch.nn.ReLU)
#
#logger_kwargs = dict(output_dir='~/spinup/data/', exp_name='SAC_MT10')
#
#sac_pytorch(env_fn=env_fn, ac_kwargs=ac_kwargs, steps_per_epoch=128 * 10, epochs=1000, start_steps=1000, lr=3e-4, logger_kwargs=logger_kwargs)
eg.add('steps_per_epoch', 5000)
# Use default hidden sizes in actor_critic function, comment below out
eg.add('ac_kwargs:hidden_sizes', [(16,16)], 'hid')
eg.add('ac_kwargs:activation', [tf.nn.relu], '')
eg.run(algo[i], num_cpu=args.cpu)
'''
#Training
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=1)
parser.add_argument('--num_runs', type=int, default=1)
args = parser.parse_args()
eg = ExperimentGrid(name=ex_number+'_ddpg_100ep')
eg.add('env_name', env_name, '', True)
eg.add('seed', [10*i for i in range(args.num_runs)])
eg.add('epochs', 100)
#eg.add('steps_per_epoch', 4000)
eg.add('max_ep_len', 1500)
eg.add('ac_kwargs:activation', [tf.nn.relu], '')
eg.add('ac_kwargs:hidden_sizes', [(64,64)], 'hid')
eg.run(ddpg, num_cpu=args.cpu)
from spinup.utils.run_utils import ExperimentGrid
from spinup import ppo
import torch
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--num_runs', type=int, default=3)
args = parser.parse_args()
eg = ExperimentGrid(name='ppo-bench')
eg.add('env_name', 'CartPole-v0', '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 10)
eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', [(32, ), (64, 64)], 'hid')
eg.add('ac_kwargs:activation', [torch.tanh, torch.relu], '')
eg.run(ppo, num_cpu=args.cpu)
from spinup.utils.run_utils import ExperimentGrid
from spinup import ppo_tf1
import tensorflow as tf
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--num_runs', type=int, default=3)
args = parser.parse_args()
eg = ExperimentGrid(name='ppo-tf1-bench')
eg.add('env_name', 'CartPole-v0', '', True)
eg.add('seed', [10 * i for i in range(args.num_runs)])
eg.add('epochs', 10)
eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', [(32, ), (64, 64)], 'hid')
eg.add('ac_kwargs:activation', [tf.tanh, tf.nn.relu], '')
eg.run(ppo_tf1, num_cpu=args.cpu)
def parse_and_execute_grid_search(cmd, args):
"""Interprets algorithm name and cmd line args into an ExperimentGrid."""
if cmd in BASE_ALGO_NAMES:
backend = DEFAULT_BACKEND[cmd]
print('\n\nUsing default backend (%s) for %s.\n'%(backend, cmd))
cmd = cmd + '_' + backend
algo = eval('spinup.'+cmd)
# Before all else, check to see if any of the flags is 'help'.
valid_help = ['--help', '-h', 'help']
if any([arg in valid_help for arg in args]):
print('\n\nShowing docstring for spinup.'+cmd+':\n')
print(algo.__doc__)
sys.exit()
def process(arg):
# Process an arg by eval-ing it, so users can specify more
# than just strings at the command line (eg allows for
# users to give functions as args).
try:
return eval(arg)
except:
return arg
# Make first pass through args to build base arg_dict. Anything
# with a '--' in front of it is an argument flag and everything after,
# until the next flag, is a possible value.
arg_dict = dict()
for i, arg in enumerate(args):
assert i > 0 or '--' in arg, \
friendly_err("You didn't specify a first flag.")
if '--' in arg:
arg_key = arg.lstrip('-')
arg_dict[arg_key] = []
else:
arg_dict[arg_key].append(process(arg))
# Make second pass through, to catch flags that have no vals.
# Assume such flags indicate that a boolean parameter should have
# value True.
for k,v in arg_dict.items():
if len(v) == 0:
v.append(True)
# Third pass: check for user-supplied shorthands, where a key has
# the form --keyname[kn]. The thing in brackets, 'kn', is the
# shorthand. NOTE: modifying a dict while looping through its
# contents is dangerous, and breaks in 3.6+. We loop over a fixed list
# of keys to avoid this issue.
given_shorthands = dict()
fixed_keys = list(arg_dict.keys())
for k in fixed_keys:
p1, p2 = k.find('['), k.find(']')
if p1 >= 0 and p2 >= 0:
# Both '[' and ']' found, so shorthand has been given
k_new = k[:p1]
shorthand = k[p1+1:p2]
given_shorthands[k_new] = shorthand
arg_dict[k_new] = arg_dict[k]
del arg_dict[k]
# Penultimate pass: sugar. Allow some special shortcuts in arg naming,
# eg treat "env" the same as "env_name". This is super specific
# to Spinning Up implementations, and may be hard to maintain.
# These special shortcuts are described by SUBSTITUTIONS.
for special_name, true_name in SUBSTITUTIONS.items():
if special_name in arg_dict:
# swap it in arg dict
arg_dict[true_name] = arg_dict[special_name]
del arg_dict[special_name]
if special_name in given_shorthands:
# point the shortcut to the right name
given_shorthands[true_name] = given_shorthands[special_name]
del given_shorthands[special_name]
# Final pass: check for the special args that go to the 'run' command
# for an experiment grid, separate them from the arg dict, and make sure
# that they have unique values. The special args are given by RUN_KEYS.
run_kwargs = dict()
for k in RUN_KEYS:
if k in arg_dict:
val = arg_dict[k]
assert len(val) == 1, \
friendly_err("You can only provide one value for %s."%k)
run_kwargs[k] = val[0]
del arg_dict[k]
# Determine experiment name. If not given by user, will be determined
# by the algorithm name.
if 'exp_name' in arg_dict:
assert len(arg_dict['exp_name']) == 1, \
friendly_err("You can only provide one value for exp_name.")
exp_name = arg_dict['exp_name'][0]
del arg_dict['exp_name']
else:
exp_name = 'cmd_' + cmd
# Make sure that if num_cpu > 1, the algorithm being used is compatible
# with MPI.
if 'num_cpu' in run_kwargs and not(run_kwargs['num_cpu'] == 1):
assert cmd in add_with_backends(MPI_COMPATIBLE_ALGOS), \
friendly_err("This algorithm can't be run with num_cpu > 1.")
# Special handling for environment: make sure that env_name is a real,
# registered gym environment.
valid_envs = [e.id for e in list(gym.envs.registry.all())]
assert 'env_name' in arg_dict, \
friendly_err("You did not give a value for --env_name! Add one and try again.")
for env_name in arg_dict['env_name']:
err_msg = dedent("""
%s is not registered with Gym.
Recommendations:
* Check for a typo (did you include the version tag?)
* View the complete list of valid Gym environments at
https://gym.openai.com/envs/
"""%env_name)
assert env_name in valid_envs, err_msg
# Construct and execute the experiment grid.
eg = ExperimentGrid(name=exp_name)
for k,v in arg_dict.items():
eg.add(k, v, shorthand=given_shorthands.get(k))
eg.run(algo, **run_kwargs)
eg.add('epochs', 10)
eg.add('steps_per_epoch', 5000)
# Use default hidden sizes in actor_critic function, comment below out
eg.add('ac_kwargs:hidden_sizes', [(32,)], 'hid')
eg.add('ac_kwargs:activation', [tf.nn.relu], '')
eg.run(algo[i], num_cpu=args.cpu)
'''
#Training
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=1)
parser.add_argument('--num_runs', type=int, default=1)
args = parser.parse_args()
eg = ExperimentGrid(name='ex4_trpo_30ep')
eg.add('env_name', 'Acrobot-v1', '', True)
eg.add('seed', [10*i for i in range(args.num_runs)])
eg.add('epochs', 30)
#eg.add('steps_per_epoch', 4000)
eg.add('max_ep_len', 1500)
eg.add('ac_kwargs:activation', [tf.nn.relu], '')
eg.add('ac_kwargs:hidden_sizes', [(16,),(16,16),(8,),(8,8),(4,),(4,4)], 'hid')
eg.run(trpo, num_cpu=args.cpu)
