def run_task(*_):
"""Implement the run_task method needed to run experiments with rllab."""
pass_params = (env_name, sumo_params, vehicles, env_params, net_params,
initial_config, scenario)
env = GymEnv(env_name, record_video=False, register_params=pass_params)
horizon = env.horizon
env = normalize(env)
policy = GaussianGRUPolicy(env_spec=env.spec, hidden_sizes=(64, ))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=horizon * 32 * 2,
max_path_length=horizon,
# whole_paths=True,
n_itr=400,
discount=0.999,
# step_size=0.01,
)
algo.train()
def run_task(*_):
env = normalize(
GymEnv("DartHopperRSS-v1", record_log=False, record_video=False))
mp_dim = 1
#policy_pre = joblib.load('data/trained/gradient_temp/rl_split_hopper_3models_taskinput_6432net_sd4_splitstd_maskedgrad_specbaseline_40k_70_30_unweighted_accumulate_gradient/final_policy_0.1.pkl')
split_dim = 0
policy = GaussianGRUPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(64, ),
)
print('trainable parameter size: ',
policy.get_param_values(trainable=True).shape)
baseline = LinearFeatureBaseline(env_spec=env.spec, additional_dim=0)
#policy = params['policy']
#baseline = params['baseline']
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=75000,
max_path_length=env.horizon,
n_itr=800,
discount=0.99,
step_size=0.01,
gae_lambda=0.98,
#mp_dim = mp_dim,
#epopt_epsilon = 1.0,
#epopt_after_iter = 0,
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
whole_paths=False,
)
algo.train()
def run_task(*_):
env = normalize(CartpoleEnv())
policy = GaussianGRUPolicy(env_spec=env.spec, )
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=10,
discount=0.99,
step_size=0.01,
optimizer=ConjugateGradientOptimizer(
hvp_approach=FiniteDifferenceHvp(base_eps=1e-5)))
algo.train()
from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.box2d.cartpole_env import CartpoleEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_gru_policy import GaussianGRUPolicy
from rllab.optimizers.conjugate_gradient_optimizer import ConjugateGradientOptimizer, FiniteDifferenceHvp
from rllab.misc.instrument import stub, run_experiment_lite
stub(globals())
env = normalize(CartpoleEnv())
policy = GaussianGRUPolicy(env_spec=env.spec, )
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=10,
discount=0.99,
step_size=0.01,
optimizer=ConjugateGradientOptimizer(
hvp_approach=FiniteDifferenceHvp(base_eps=1e-5)))
run_experiment_lite(
algo.train(),
n_parallel=1,
seed=1,
)
def run_task(*_):
"""Implement the run_task method needed to run experiments with rllab."""
sumo_params = SumoParams(sim_step=0.1, sumo_binary="sumo", seed=0)
vehicles = Vehicles()
vehicles.add(veh_id="rl",
acceleration_controller=(RLController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=1)
vehicles.add(veh_id="idm",
acceleration_controller=(IDMController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=21)
additional_env_params = {
"target_velocity": 8,
"ring_length": [220, 270],
"max_accel": 1,
"max_decel": 1
}
env_params = EnvParams(horizon=HORIZON,
additional_params=additional_env_params,
warmup_steps=750)
additional_net_params = {
"length": 260,
"lanes": 1,
"speed_limit": 30,
"resolution": 40
}
net_params = NetParams(additional_params=additional_net_params)
initial_config = InitialConfig(spacing="uniform", bunching=50)
print("XXX name", exp_tag)
scenario = LoopScenario(exp_tag,
CircleGenerator,
vehicles,
net_params,
initial_config=initial_config)
env_name = "WaveAttenuationPOEnv"
pass_params = (env_name, sumo_params, vehicles, env_params, net_params,
initial_config, scenario)
env = GymEnv(env_name, record_video=False, register_params=pass_params)
horizon = env.horizon
env = normalize(env)
policy = GaussianGRUPolicy(
env_spec=env.spec,
hidden_sizes=(5, ),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=3600 * 72 * 2,
max_path_length=horizon,
n_itr=5,
# whole_paths=True,
discount=0.999,
# step_size=v["step_size"],
)
algo.train(),
discriminator = Mlp_Discriminator( a_max=0.2, a_min=0.2, disc_window=obs_window, iteration=total_iter, disc_joints_dim=2, hidden_sizes=(8, 8))
# initializing
env = normalize(SimpleHumanoidEnv(discriminator=discriminator, window=obs_window), normalize_obs=True)
if Policy=="MLP":
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(100, 50, 25)
)
if Policy=="GRU":
policy=GaussianGRUPolicy(
env_spec=env.spec,
hidden_sizes=(64,),
state_include_action=False,
hidden_nonlinearity=NL.tanh)
if policy==None:
print("not valid policy type")
else:
base_line_optimizer = ConjugateGradientOptimizer()
baseline = GaussianMLPBaseline(env.spec,
regressor_args={
"mean_network": None,
"hidden_sizes": (100, 50, 25),
"hidden_nonlinearity": NL.tanh,
"optimizer": base_line_optimizer,
"use_trust_region": True,
"step_size": 0.01,
def main():
now = datetime.datetime.now(dateutil.tz.tzlocal())
rand_id = str(uuid.uuid4())[:5]
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z')
default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
parser = argparse.ArgumentParser()
parser.add_argument('--exp_name',
type=str,
default=default_exp_name,
help='Name of the experiment.')
parser.add_argument('--discount', type=float, default=0.95)
parser.add_argument('--gae_lambda', type=float, default=0.99)
parser.add_argument('--n_iter', type=int, default=250)
parser.add_argument('--sampler_workers', type=int, default=1)
parser.add_argument('--max_traj_len', type=int, default=250)
parser.add_argument('--update_curriculum',
action='store_true',
default=False)
parser.add_argument('--n_timesteps', type=int, default=8000)
parser.add_argument('--control', type=str, default='centralized')
parser.add_argument('--control', type=str, default='centralized')
parser.add_argument('--buffer_size', type=int, default=1)
parser.add_argument('--n_good', type=int, default=3)
parser.add_argument('--n_hostage', type=int, default=5)
parser.add_argument('--n_bad', type=int, default=5)
parser.add_argument('--n_coop_save', type=int, default=2)
parser.add_argument('--n_coop_avoid', type=int, default=2)
parser.add_argument('--n_sensors', type=int, default=20)
parser.add_argument('--sensor_range', type=float, default=0.2)
parser.add_argument('--save_reward', type=float, default=3)
parser.add_argument('--hit_reward', type=float, default=-1)
parser.add_argument('--encounter_reward', type=float, default=0.01)
parser.add_argument('--bomb_reward', type=float, default=-10.)
parser.add_argument('--recurrent', action='store_true', default=False)
parser.add_argument('--baseline_type', type=str, default='linear')
parser.add_argument('--policy_hidden_sizes', type=str, default='128,128')
parser.add_argument('--baselin_hidden_sizes', type=str, default='128,128')
parser.add_argument('--max_kl', type=float, default=0.01)
parser.add_argument('--log_dir', type=str, required=False)
parser.add_argument('--tabular_log_file',
type=str,
default='progress.csv',
help='Name of the tabular log file (in csv).')
parser.add_argument('--text_log_file',
type=str,
default='debug.log',
help='Name of the text log file (in pure text).')
parser.add_argument('--params_log_file',
type=str,
default='params.json',
help='Name of the parameter log file (in json).')
parser.add_argument('--seed', type=int, help='Random seed for numpy')
parser.add_argument('--args_data',
type=str,
help='Pickled data for stub objects')
parser.add_argument('--snapshot_mode',
type=str,
default='all',
help='Mode to save the snapshot. Can be either "all" '
'(all iterations will be saved), "last" (only '
'the last iteration will be saved), or "none" '
'(do not save snapshots)')
parser.add_argument(
'--log_tabular_only',
type=ast.literal_eval,
default=False,
help=
'Whether to only print the tabular log information (in a horizontal format)'
)
args = parser.parse_args()
parallel_sampler.initialize(n_parallel=args.sampler_workers)
if args.seed is not None:
set_seed(args.seed)
parallel_sampler.set_seed(args.seed)
args.hidden_sizes = tuple(map(int, args.policy_hidden_sizes.split(',')))
centralized = True if args.control == 'centralized' else False
sensor_range = np.array(map(float, args.sensor_range.split(',')))
assert sensor_range.shape == (args.n_pursuers, )
env = ContinuousHostageWorld(args.n_good,
args.n_hostage,
args.n_bad,
args.n_coop_save,
args.n_coop_avoid,
n_sensors=args.n_sensors,
sensor_range=args.sensor_range,
save_reward=args.save_reward,
hit_reward=args.hit_reward,
encounter_reward=args.encounter_reward,
bomb_reward=args.bomb_reward)
env = RLLabEnv(StandardizedEnv(env), mode=args.control)
if args.buffer_size > 1:
env = ObservationBuffer(env, args.buffer_size)
if args.recurrent:
policy = GaussianGRUPolicy(env_spec=env.spec,
hidden_sizes=args.hidden_sizes)
else:
policy = GaussianMLPPolicy(env_spec=env.spec,
hidden_sizes=args.hidden_sizes)
if args.baseline_type == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
else:
baseline = ZeroBaseline(obsfeat_space)
# logger
default_log_dir = config.LOG_DIR
if args.log_dir is None:
log_dir = osp.join(default_log_dir, args.exp_name)
else:
log_dir = args.log_dir
tabular_log_file = osp.join(log_dir, args.tabular_log_file)
text_log_file = osp.join(log_dir, args.text_log_file)
params_log_file = osp.join(log_dir, args.params_log_file)
logger.log_parameters_lite(params_log_file, args)
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
prev_snapshot_dir = logger.get_snapshot_dir()
prev_mode = logger.get_snapshot_mode()
logger.set_snapshot_dir(log_dir)
logger.set_snapshot_mode(args.snapshot_mode)
logger.set_log_tabular_only(args.log_tabular_only)
logger.push_prefix("[%s] " % args.exp_name)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.n_timesteps,
max_path_length=args.max_traj_len,
n_itr=args.n_iter,
discount=args.discount,
step_size=args.max_kl,
mode=args.control,
)
algo.train()
from madrl_environments import StandardizedEnv
from madrl_environments.pursuit import MAWaterWorld
from rllabwrapper import RLLabEnv
from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.policies.gaussian_gru_policy import GaussianGRUPolicy
env = StandardizedEnv(MAWaterWorld(3, 10, 2, 5))
env = RLLabEnv(env)
policy = GaussianGRUPolicy(env_spec=env.spec, hidden_sizes=(32,))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env=env,
policy=policy,
baseline=baseline,
batch_size=8000,
max_path_length=200,
n_itr=500,
discount=0.99,
step_size=0.01,
mode='decentralized',)
algo.train()
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.policies.gaussian_gru_policy import GaussianGRUPolicy
from rllab.envs.gym_env import GymEnv
import rllab.misc.logger as logger
env = normalize(GymEnv('BipedalWalker-v2'))
#policy = GaussianMLPPolicy(
# env_spec=env.spec,
# # The neural network policy should have two hidden layers, each with 32 hidden units.
# hidden_sizes=(64, 64)
#)
policy = GaussianGRUPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, ))
baseline = LinearFeatureBaseline(env_spec=env.spec)
# logger
LOG_DIR = 'walker_gru_test'
tabular_log_file = osp.join(LOG_DIR, 'progress.csv')
text_log_file = osp.join(LOG_DIR, 'debug.log')
params_log_file = osp.join(LOG_DIR, 'params.json')
logger.add_text_output(text_log_file)
logger.add_tabular_output(tabular_log_file)
logger.set_snapshot_dir(LOG_DIR)
logger.set_snapshot_mode('last')
from rllab.envs.box2d.cartpole_env import CartpoleEnv from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline from rllab.policies.gaussian_gru_policy import GaussianGRUPolicy from rllab.envs.normalized_env import normalize import numpy as np import theano import theano.tensor as TT from lasagne.updates import adam import pdb # normalize() makes sure that the actions for the environment lies # within the range [-1, 1] (only works for environments with continuous actions) env = normalize(CartpoleEnv()) # Initialize a neural network policy with a single hidden layer of 8 hidden units policy = GaussianGRUPolicy(env.spec) # Initialize a linear baseline estimator using default hand-crafted features baseline = LinearFeatureBaseline(env.spec) # We will collect 100 trajectories per iteration N = 100 # Each trajectory will have at most 100 time steps T = 100 # Number of iterations n_itr = 100 # Set the discount factor for the problem discount = 0.99 # Learning rate for the gradient update learning_rate = 0.1 # Construct the computation graph
