gforce_threshold=1.0,
# https://iopscience.iop.org/article/10.1088/0143-0807/37/6/065008/pdf
# Importantly they depict the threshold
# for admissible acceleration onset or jerk as j = 15g/s or ~150m/s^3.
jerk_threshold=150.0, # 15g/s
incent_win=True,
constrain_controls=False,
incent_yield_to_oncoming_traffic=True,
physics_steps_per_observation=12,
discrete_actions=COMFORTABLE_ACTIONS,
)
net_config = dict(hidden_units=(256, 256), activation=torch.nn.Tanh)
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_from_scratch_discrete_steer_and_accel/intersection_from_scratch_discrete_steer_and_accel_s0_2020_04-22_13-12.42.071188'
)
# eg.add('reinitialize_optimizer_on_resume', True)
# eg.add('num_inputs_to_add', 0)
# eg.add('pi_lr', 3e-6)
# eg.add('vf_lr', 1e-5)
# eg.add('boost_explore', 5)
eg.add('epochs', 20000)
eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
for setting in settings:
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)
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)
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument('--num_runs', type=int, default=1)
parser.add_argument('--psp_type',
type=str,
default='Rand',
help='Rand, Ones, Binary, Proposed, Sanity')
parser.add_argument('--hidden_sizes',
type=int,
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)
logger.log_tabular('Time', time.time() - start_time)
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.algos.ddpg.ddpg import ddpg
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,
# 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=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')
# 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=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
], '')
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 fgym_trunk_sac_discrete_v2
import torch
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--cpu', type=int, default=1)
parser.add_argument('--num_runs', type=int, default=2)
args = parser.parse_args()
eg = ExperimentGrid(name='sac-pyt')
from src.Financial_gym.financial_env.fgym import *
env = lambda: Assembly_Fin_for_pic(
data_path='src/Financial_gym/data/pic/', # 文件存放路径
game=continus_Daily_Fin_Futures_holding_reward_pic,
seed=123,
windows=50,
init_capital=1000000,
show_statistics=True,
drawdown=0.1)
eg.add('environment', env)
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
from spinup.utils.run_utils import ExperimentGrid
from spinup import ppo_pytorch
import torch
from gym_match_input_continuous.experiments import utils
experiment_name = os.path.basename(__file__)[:-3]
notes = """
Garbage into RL treasure out
"""
env_config = dict(env_name='match-input-continuous-v0', )
net_config = dict(hidden_units=(32, 32), activation=torch.nn.Tanh)
eg = ExperimentGrid(name=experiment_name)
eg.add('env_name', env_config['env_name'], '', False)
# eg.add('gamma', 0.999) # Lower gamma so seconds of effective horizon remains at 10s with current physics steps = 12 * 1/60s * 1 / (1-gamma)
eg.add('epochs', 1000)
eg.add('steps_per_epoch', 500)
eg.add('try_rollouts', 2)
eg.add('shift_advs_pct', 90)
eg.add('take_worst_rollout', True)
eg.add('steps_per_try_rollout', 1)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
# 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)
expect_normalized_action_deltas=False,
jerk_penalty_coeff=0.20 / (60*100),
gforce_penalty_coeff=0.06,
collision_penalty_coeff=4,
gforce_threshold=None,
incent_win=True,
constrain_controls=False,
incent_yield_to_oncoming_traffic=True,
)
net_config = dict(
hidden_units=(256, 256),
activation=torch.nn.Tanh
)
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_fine_tune_add_left_yield/intersection_2_agents_fine_tune_add_left_yield_s0_2020_03-23_13-16.15')
eg.add('reinitialize_optimizer_on_resume', False) # Old optimizer had only 28 inputs despite NN having 29!
eg.add('num_inputs_to_add', 0)
eg.add('pi_lr', 3e-6)
eg.add('vf_lr', 1e-5)
eg.add('boost_explore', 5)
eg.add('epochs', 8000)
eg.add('steps_per_epoch', 32000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
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)
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)
env_config = dict(
env_name='deepdrive-2d-intersection-w-gs-allow-decel-v0',
is_intersection_map=True,
expect_normalized_action_deltas=False,
jerk_penalty_coeff=1.5 * 0.20 / (60 * 100),
gforce_penalty_coeff=1.5 * 0.06,
collision_penalty_coeff=1.5,
end_on_harmful_gs=False,
incent_win=True,
constrain_controls=False,
)
net_config = dict(hidden_units=(256, 256), activation=torch.nn.Tanh)
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_fine_tune_collision_resume_add_comfort1/intersection_2_agents_fine_tune_collision_resume_add_comfort1_s0_2020_03-13_21-45.39'
)
eg.add('reinitialize_optimizer_on_resume', True)
eg.add(
'pi_lr', 3e-6
) # 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-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('epochs', 8000)
eg.add('steps_per_epoch', 16000)
parser.add_argument('--cpu', type=str, default='auto')
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)
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('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)
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)
env_config = dict(
env_name='deepdrive-2d-intersection-w-gs-allow-decel-v0',
is_intersection_map=True,
expect_normalized_action_deltas=False,
jerk_penalty_coeff=0.20 / (60 * 100),
gforce_penalty_coeff=0.06,
collision_penalty_coeff=4,
end_on_harmful_gs=False,
incent_win=True,
constrain_controls=False,
incent_yield_to_oncoming_traffic=True,
)
net_config = dict(hidden_units=(256, 256), activation=torch.nn.Tanh)
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_fine_tune_add_left_yield2/intersection_2_agents_fine_tune_add_left_yield2_s0_2020_03-23_22-40.11'
)
eg.add('reinitialize_optimizer_on_resume', True)
eg.add('num_inputs_to_add', 0)
eg.add('pi_lr', 3e-6)
eg.add('vf_lr', 1e-5)
# eg.add('boost_explore', 5)
eg.add('epochs', 8000)
eg.add('steps_per_epoch', 32000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
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)
env_config = dict(
env_name='deepdrive-2d-intersection-w-gs-allow-decel-v0',
is_intersection_map=True,
expect_normalized_action_deltas=False,
jerk_penalty_coeff=0,
gforce_penalty_coeff=0,
collision_penalty_coeff=1,
end_on_harmful_gs=False,
incent_win=True,
constrain_controls=False,
)
net_config = dict(hidden_units=(256, 256), activation=torch.nn.Tanh)
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_03-12_18-09.49'
)
eg.add('reinitialize_optimizer_on_resume', True)
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)
lane_margin=0.2,
# https://iopscience.iop.org/article/10.1088/0143-0807/37/6/065008/pdf
# Importantly they depict the threshold
# for admissible acceleration onset or jerk as j = 15g/s or ~150m/s^3.
jerk_threshold=150.0, # 15g/s
incent_win=True,
constrain_controls=False,
incent_yield_to_oncoming_traffic=True,
physics_steps_per_observation=12,
discrete_actions=COMFORTABLE_ACTIONS2,
)
net_config = dict(hidden_units=(256, 256), activation=torch.nn.Tanh)
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_discrete_micro_turn_lower_lane_pen2_diag_lane19/intersection_discrete_micro_turn_lower_lane_pen2_diag_lane19_s0_2020_05-15_18-26.32.741210'
)
# eg.add('reinitialize_optimizer_on_resume', True)
# eg.add('num_inputs_to_add', 0)
eg.add('pi_lr', 3e-4) # default pi_lr=3e-4
eg.add('vf_lr', 1e-3) # default vf_lr=1e-3,
# eg.add('boost_explore', 5)
eg.add('epochs', 20000)
eg.add('steps_per_epoch', 8000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
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)
# Importantly they depict the threshold
# for admissible acceleration onset or jerk as j = 15g/s or ~150m/s^3.
jerk_threshold=150.0, # 15g/s
incent_win=True,
constrain_controls=False,
incent_yield_to_oncoming_traffic=True,
physics_steps_per_observation=12,
discrete_actions=COMFORTABLE_ACTIONS2,
)
net_config = dict(
hidden_units=(256, 256),
activation=torch.nn.Tanh
)
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_discrete_micro_turn_lower_lane_pen2_diag_lane17_2/intersection_discrete_micro_turn_lower_lane_pen2_diag_lane17_2_s0_2020_05-13_14-46.12.823730_snapshot2')
# eg.add('reinitialize_optimizer_on_resume', True)
# eg.add('num_inputs_to_add', 0)
# eg.add('pi_lr', 3e-6)
# eg.add('vf_lr', 1e-5)
# eg.add('boost_explore', 5)
eg.add('epochs', 20000)
eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
# Importantly they depict the threshold
# for admissible acceleration onset or jerk as j = 15g/s or ~150m/s^3.
jerk_threshold=150.0, # 15g/s
incent_win=True,
constrain_controls=False,
incent_yield_to_oncoming_traffic=True,
physics_steps_per_observation=12,
discrete_actions=COMFORTABLE_ACTIONS2,
)
net_config = dict(
hidden_units=(256, 256),
activation=torch.nn.Tanh
)
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_fine_tune_add_left_yield2/intersection_2_agents_fine_tune_add_left_yield2_s0_2020_03-23_22-40.11')
# eg.add('reinitialize_optimizer_on_resume', True)
# eg.add('num_inputs_to_add', 0)
# eg.add('pi_lr', 3e-6)
# eg.add('vf_lr', 1e-5)
# eg.add('boost_explore', 5)
eg.add('epochs', 20000)
eg.add('steps_per_epoch', 4000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
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)
jerk_penalty_coeff=3.3e-5,
gforce_penalty_coeff=0.006 * 5,
collision_penalty_coeff=4,
lane_penalty_coeff=0.02,
speed_reward_coeff=0.50,
gforce_threshold=None,
incent_win=True,
constrain_controls=False,
incent_yield_to_oncoming_traffic=True,
physics_steps_per_observation=12,
discrete_actions=COMFORTABLE_ACTIONS,
)
net_config = dict(hidden_units=(64, 64), activation=torch.nn.Tanh)
eg = ExperimentGrid(name=experiment_name)
eg.add('env_name', env_config['env_name'], '', False)
pso = env_config['physics_steps_per_observation']
effective_horizon_seconds = 10
eg.add(
'gamma', 1 - pso / (effective_horizon_seconds * FPS)
) # Lower gamma so seconds of effective horizon remains at 10s with current physics steps = 12 * 1/60s * 1 / (1-gamma)
eg.add('epochs', 10000)
eg.add('steps_per_epoch', 8000)
eg.add('ac_kwargs:hidden_sizes', net_config['hidden_units'], 'hid')
eg.add('ac_kwargs:activation', net_config['activation'], '')
eg.add('notes', notes, '')
eg.add('run_filename', os.path.realpath(__file__), '')
eg.add('env_config', env_config, '')
remainder = setting_number
for setting in settings:
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)
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(
