def run_task(*_):
# Please note that different environments with different action spaces may require different
# policies. For example with a Box action space, a GaussianMLPPolicy works, but for a Discrete
# action space may need to use a CategoricalMLPPolicy (see the trpo_gym_cartpole.py example)
env = normalize(GymEnv("Pendulum-v0"))
policy = GaussianMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32)
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=env.horizon,
n_itr=50,
discount=0.99,
step_size=0.01,
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
algo.train()
def test_issue_3():
"""
As reported in https://github.com/rllab/rllab/issues/3, the adaptive_std parameter was not functioning properly
"""
env = CartpoleEnv()
policy = GaussianMLPPolicy(env_spec=env, adaptive_std=True)
baseline = ZeroBaseline(env_spec=env.spec)
algo = TRPO(env=env, policy=policy, baseline=baseline, batch_size=100, n_itr=1)
algo.train()
def test_trpo_relu_nan():
env = DummyEnv()
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_nonlinearity=naive_relu,
hidden_sizes=(1,))
baseline = ZeroBaseline(env_spec=env.spec)
algo = TRPO(
env=env, policy=policy, baseline=baseline, n_itr=1, batch_size=1000, max_path_length=100,
step_size=0.001
)
algo.train()
assert not np.isnan(np.sum(policy.get_param_values()))
def test_trpo_deterministic_nan():
env = DummyEnv()
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(1,))
policy._l_log_std.param.set_value([np.float32(np.log(1e-8))])
baseline = ZeroBaseline(env_spec=env.spec)
algo = TRPO(
env=env, policy=policy, baseline=baseline, n_itr=10, batch_size=1000, max_path_length=100,
step_size=0.01
)
algo.train()
assert not np.isnan(np.sum(policy.get_param_values()))
def run_experiment(**params):
base_params = copy.copy(DEFAULTS)
base_params.update(params)
params = base_params
pprint(params)
grid_world = SlaveGridWorldEnv("walled_chain",
max_traj_length=DEFAULTS["max_path_length"],
goal_reward=params["goal_reward"])
agent = GridWorldMasterAgent(grid_world, match_reward=params["match_reward"])
env = normalize(SituatedConversationEnvironment(env=grid_world, b_agent=agent))
baseline = LinearFeatureBaseline(env)
policy = RecurrentCategoricalPolicy(
name="policy",
env_spec=env.spec,
hidden_dims=params["policy_hidden_dims"],
feature_network=MLPNetworkWithEmbeddings(
"feature_network", env.observation_space.flat_dim,
params["feature_dim"], params["feature_hidden_dims"],
tf.tanh, tf.tanh, agent.vocab_size, params["embedding_dim"]),
state_include_action=False,
)
optimizer = ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=params["batch_size"],
max_path_length=params["max_path_length"],
n_itr=params["n_itr"],
discount=0.99,
step_size=params["step_size"],
optimizer=optimizer,
)
run_experiment_lite(
algo.train(),
n_parallel=15,
snapshot_mode="last",
exp_prefix="grid_world_sweep3",
variant=params,
)
def run_task(*_):
env = normalize(GymEnv("Pendulum-v0"))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32)
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=env.horizon,
n_itr=50,
discount=0.99,
step_size=0.01,
plot=True,
)
algo.train()
def run_experiment(**params):
base_params = copy.copy(DEFAULTS)
base_params.update(params)
params = base_params
grid_world = SlaveGridWorldEnv("3x3", goal_reward=params["goal_reward"])
env = normalize(grid_world)
baseline = LinearFeatureBaseline(env)
policy = CategoricalMLPPolicy(
name="policy",
env_spec=env.spec,
hidden_sizes=params["policy_hidden_dims"],
)
optimizer = ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=params["batch_size"],
max_path_length=5,
n_itr=params["n_itr"],
discount=0.99,
step_size=params["step_size"],
optimizer=optimizer,
)
run_experiment_lite(
algo.train(),
n_parallel=5,
snapshot_mode="last",
exp_prefix="grid_world_silent",
variant=params,
)
def run_task(v):
env = normalize(CartpoleEnv())
policy = GaussianMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32)
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=40,
discount=0.99,
step_size=v["step_size"],
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
algo.train()
def run_task(*_):
auton_cars = 20
sumo_params = SumoParams(time_step=0.1,
human_speed_mode="no_collide",
rl_speed_mode="no_collide",
sumo_binary="sumo-gui")
vehicles = Vehicles()
vehicles.add_vehicles("idm", (RLController, {}), None, None, 0, 20)
intensity = .2
v_enter = 10
env_params = EnvParams(additional_params={
"target_velocity": v_enter,
"control-length": 150,
"max_speed": v_enter
})
additional_net_params = {
"horizontal_length_in": 400,
"horizontal_length_out": 800,
"horizontal_lanes": 1,
"vertical_length_in": 400,
"vertical_length_out": 800,
"vertical_lanes": 1,
"speed_limit": {
"horizontal": v_enter,
"vertical": v_enter
}
}
net_params = NetParams(no_internal_links=False,
additional_params=additional_net_params)
cfg_params = {"start_time": 0, "end_time": 3000, "cfg_path": "debug/cfg/"}
initial_config = InitialConfig(spacing="custom",
additional_params={
"intensity": intensity,
"enter_speed": v_enter
})
scenario = TwoWayIntersectionScenario("two-way-intersection",
TwoWayIntersectionGenerator,
vehicles,
net_params,
initial_config=initial_config)
env = TwoIntersectionEnvironment(env_params, sumo_params, scenario)
env_name = "TwoIntersectionEnvironment"
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)
logging.info("Experiment Set Up complete")
print("experiment initialized")
env = normalize(env)
policy = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(64, 64))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=30000,
max_path_length=horizon,
# whole_paths=True,
n_itr=200,
discount=0.999,
# step_size=0.01,
)
algo.train()
policy = CategoricalMLPPolicy(env_spec=env.spec, )
baseline = LinearFeatureBaseline(env_spec=env.spec)
# bonus_evaluators = [GridBonusEvaluator(mesh_density=mesh_density, visitation_bonus=1, snn_H_bonus=0)]
# reward_coef_bonus = [reward_coef]
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
self_normalize=True,
log_deterministic=True,
# reward_coef=reward_coef,
# bonus_evaluator=bonus_evaluators,
# reward_coef_bonus=reward_coef_bonus,
batch_size=1e6 / time_step_agg,
whole_paths=True,
max_path_length=1e4 / time_step_agg * maze_size_scaling /
2., # correct for larger envs
n_itr=200,
discount=0.99,
step_size=0.01,
)
for s in [10, 20,
30]: # range(10, 110, 10): # [10, 20, 30, 40, 50]:
exp_prefix = 'hier-snn-egoSnake-maze0'
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
exp_name = exp_prefix + '{}scale_{}agg_{}pl_PRE{}_seed{}_{}'.format(
def run_task(*_):
"""Implement the run_task method needed to run experiments with rllab."""
V_ENTER = 30
INNER_LENGTH = 300
LONG_LENGTH = 100
SHORT_LENGTH = 300
N_ROWS = 3
N_COLUMNS = 3
NUM_CARS_LEFT = 1
NUM_CARS_RIGHT = 1
NUM_CARS_TOP = 1
NUM_CARS_BOT = 1
tot_cars = (NUM_CARS_LEFT + NUM_CARS_RIGHT) * N_COLUMNS \
+ (NUM_CARS_BOT + NUM_CARS_TOP) * N_ROWS
grid_array = {
"short_length": SHORT_LENGTH,
"inner_length": INNER_LENGTH,
"long_length": LONG_LENGTH,
"row_num": N_ROWS,
"col_num": N_COLUMNS,
"cars_left": NUM_CARS_LEFT,
"cars_right": NUM_CARS_RIGHT,
"cars_top": NUM_CARS_TOP,
"cars_bot": NUM_CARS_BOT
}
sim_params = SumoParams(sim_step=1, render=True)
vehicles = VehicleParams()
vehicles.add(veh_id="idm",
acceleration_controller=(SimCarFollowingController, {}),
car_following_params=SumoCarFollowingParams(
min_gap=2.5,
tau=1.1,
max_speed=V_ENTER,
speed_mode="all_checks"),
routing_controller=(GridRouter, {}),
num_vehicles=tot_cars)
tl_logic = TrafficLightParams(baseline=False)
additional_env_params = {
"target_velocity": 50,
"switch_time": 3.0,
"num_observed": 2,
"discrete": False,
"tl_type": "controlled"
}
env_params = EnvParams(additional_params=additional_env_params)
additional_net_params = {
"speed_limit": 35,
"grid_array": grid_array,
"horizontal_lanes": 1,
"vertical_lanes": 1
}
if USE_INFLOWS:
initial_config, net_params = get_flow_params(
v_enter=V_ENTER,
vehs_per_hour=EDGE_INFLOW,
col_num=N_COLUMNS,
row_num=N_ROWS,
add_net_params=additional_net_params)
else:
initial_config, net_params = get_non_flow_params(
V_ENTER, additional_net_params)
scenario = SimpleGridScenario(name="grid-intersection",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=tl_logic)
env_name = "PO_TrafficLightGridEnv"
pass_params = (env_name, sim_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 = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=40000,
max_path_length=horizon,
# whole_paths=True,
n_itr=800,
discount=0.999,
# step_size=0.01,
)
algo.train()
hidden_sizes=layer_size,
is_protagonist=False
)
adv_baseline = LinearFeatureBaseline(env_spec=env.spec)
## Initializing the parallel sampler ##
parallel_sampler.initialize(n_process)
## Optimizer for the Protagonist ##
pro_algo = TRPO(
env=env,
pro_policy=pro_policy,
adv_policy=adv_policy,
pro_baseline=pro_baseline,
adv_baseline=adv_baseline,
batch_size=batch_size,
max_path_length=path_length,
n_itr=n_pro_itr,
discount=0.995,
gae_lambda=gae_lambda,
step_size=step_size,
is_protagonist=True
)
## Optimizer for the Adversary ##
adv_algo = TRPO(
env=env,
pro_policy=pro_policy,
adv_policy=adv_policy,
pro_baseline=pro_baseline,
adv_baseline=adv_baseline,
batch_size=batch_size,
folder_name = 'cartpole_split_sanitycheck'
segmentation_num = 2
load_path_from_file = False
load_metric_from_file = False
split_percentage = 0.2
# generate data
baseline = LinearFeatureBaseline(env_spec=env.spec, additional_dim=0)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=10000,
max_path_length=env.horizon,
n_itr=5,
discount=0.995,
step_size=0.01,
gae_lambda=0.97,
)
algo.init_opt()
if not load_path_from_file:
init_param = policy.get_param_values()
init_param_obj = copy.deepcopy(policy.get_params())
from rllab.sampler import parallel_sampler
parallel_sampler.initialize(n_parallel=2)
def average_error(env, policy, batch_size, gt_gradient):
np.random.seed(0)
baseline = LinearFeatureBaseline(env_spec=env.spec, additional_dim=0)
init_param = policy.get_param_values()
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=batch_size,
max_path_length=env.horizon,
n_itr=5,
discount=0.995,
step_size=0.01,
gae_lambda=0.97,
)
gradients_vanilla = []
gradients_randwalk = []
gradient_error_vanilla = []
gradient_error_randwalk = []
env.wrapped_env.env.env.perturb_MP = True
algo.start_worker()
algo.init_opt()
for i in range(20):
policy.set_param_values(init_param) # reset the policy parameters
paths = algo.sampler.obtain_samples(0)
samples_data = algo.sampler.process_samples(0, paths)
samples_data = algo.sampler.process_samples(0, paths)
grad = get_gradient(algo, samples_data)
gradients_randwalk.append(grad)
gradient_error_randwalk.append(np.linalg.norm(grad - gt_gradient))
algo.shutdown_worker()
env.wrapped_env.env.env.perturb_MP = False
algo.start_worker()
algo.init_opt()
for i in range(20):
policy.set_param_values(init_param) # reset the policy parameters
paths = algo.sampler.obtain_samples(0)
samples_data = algo.sampler.process_samples(0, paths)
samples_data = algo.sampler.process_samples(0, paths)
grad = get_gradient(algo, samples_data)
gradients_vanilla.append(grad)
gradient_error_vanilla.append(np.linalg.norm(grad - gt_gradient))
algo.shutdown_worker()
print(np.std(gradients_vanilla, axis=0).shape)
print(np.linalg.norm(np.mean(gradients_vanilla, axis=0)),
np.mean(np.std(gradients_vanilla, axis=0)))
print(np.mean(gradient_error_vanilla))
print('randwalk')
print(np.linalg.norm(np.mean(gradients_randwalk, axis=0)),
np.mean(np.std(gradients_randwalk, axis=0)))
print(np.mean(gradient_error_randwalk))
return np.mean(gradient_error_vanilla), np.mean(gradient_error_randwalk)
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.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from examples.point_env import PointEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
env = normalize(PointEnv())
policy = GaussianMLPPolicy(
env_spec=env.spec,
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
)
algo.train()
def train(env, policy, policy_init, num_episodes, episode_cap, horizon,
**alg_args):
if env.startswith('rllab.'):
# Get env name and class
env_name = re.match('rllab.(\S+)', env).group(1)
env_rllab_class = rllab_env_from_name(env_name)
env = normalize(env_rllab_class())
else:
raise Exception('Only working for RLLAB envs')
# Policy initialization
if policy_init == 'zeros':
initializer = LI.Constant(0)
elif policy_init == 'normal':
initializer = LI.Normal()
else:
raise Exception('Unrecognized policy initialization.')
# Setting the policy type
if policy == 'linear':
hidden_sizes = tuple()
elif policy == 'simple-nn':
hidden_sizes = [16]
else:
raise Exception('NOT IMPLEMENTED.')
# Creating the policy
obs_dim = env.observation_space.flat_dim
action_dim = env.action_space.flat_dim
mean_network = MLP(
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=NL.tanh,
output_nonlinearity=None,
output_b_init=None,
output_W_init=initializer,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=hidden_sizes,
mean_network=mean_network,
log_weights=True,
)
# Creating baseline
baseline = LinearFeatureBaseline(env_spec=env.spec)
# Adding max_episodes constraint. If -1, this is unbounded
if episode_cap:
alg_args['max_episodes'] = num_episodes
# Run algorithm
algo = TRPO(env=env,
policy=policy,
baseline=baseline,
batch_size=horizon * num_episodes,
whole_paths=True,
max_path_length=horizon,
**alg_args)
algo.train()
print('----- ENDING ------')
print(policy.get_param_values())
def run_task(*_):
"""Implement the run_task method needed to run experiments with rllab."""
sim_params = SumoParams(sim_step=0.2, render=True)
# note that the vehicles are added sequentially by the scenario,
# so place the merging vehicles after the vehicles in the ring
vehicles = VehicleParams()
# Inner ring vehicles
vehicles.add(veh_id="human",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
lane_change_controller=(SimLaneChangeController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=6,
car_following_params=SumoCarFollowingParams(minGap=0.0,
tau=0.5),
lane_change_params=SumoLaneChangeParams())
# A single learning agent in the inner ring
vehicles.add(veh_id="rl",
acceleration_controller=(RLController, {}),
lane_change_controller=(SimLaneChangeController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=1,
car_following_params=SumoCarFollowingParams(
minGap=0.01, tau=0.5, speed_mode="no_collide"),
lane_change_params=SumoLaneChangeParams())
# Outer ring vehicles
vehicles.add(veh_id="merge-human",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
lane_change_controller=(SimLaneChangeController, {}),
routing_controller=(ContinuousRouter, {}),
num_vehicles=10,
car_following_params=SumoCarFollowingParams(minGap=0.0,
tau=0.5),
lane_change_params=SumoLaneChangeParams())
env_params = EnvParams(horizon=HORIZON,
additional_params={
"max_accel": 3,
"max_decel": 3,
"target_velocity": 10,
"n_preceding": 2,
"n_following": 2,
"n_merging_in": 2,
})
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
additional_net_params["ring_radius"] = 50
additional_net_params["inner_lanes"] = 1
additional_net_params["outer_lanes"] = 1
additional_net_params["lane_length"] = 75
net_params = NetParams(no_internal_links=False,
additional_params=additional_net_params)
initial_config = InitialConfig(x0=50,
spacing="uniform",
additional_params={"merge_bunching": 0})
scenario = TwoLoopsOneMergingScenario(name=exp_tag,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env_name = "TwoLoopsMergePOEnv"
pass_params = (env_name, sim_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 = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(100, 50, 25))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=64 * 3 * horizon,
max_path_length=horizon,
# whole_paths=True,
n_itr=1000,
discount=0.999,
# step_size=0.01,
)
algo.train()
"""
Returns a Space object
"""
low = np.array(
[0, -np.pi / 2, -np.pi / 2, 0, -np.pi, -np.pi, 0, -np.pi, -np.pi])
high = np.array([
100, np.pi / 2, np.pi / 2, 1000, np.pi, np.pi, 1000, np.pi, -np.pi
])
return Box(low=low, high=high)
def log_diagnostics(self, paths):
pass
if __name__ == "__main__":
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
env = normalize(FlightEnv())
policy = GaussianMLPPolicy(env_spec=env.spec, )
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env=env,
policy=policy,
baseline=baseline,
max_path_length=400,
batch_size=4000,
gae_lambda=0.7)
algo.train()
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
sampling_res = 0 if 'sampling_res' not in v.keys() else v['sampling_res']
# Log performance of randomly initialized policy with FIXED goal [0.1, 0.1]
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir() # problem with logger module here!!
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=3)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
tf_session = tf.Session()
inner_env = normalize(AntEnv())
uniform_goal_generator = UniformStateGenerator(state_size=v['goal_size'],
bounds=v['goal_range'],
center=v['goal_center'])
env = GoalExplorationEnv(
env=inner_env,
goal_generator=uniform_goal_generator,
obs2goal_transform=lambda x: x[-3:-1],
terminal_eps=v['terminal_eps'],
distance_metric=v['distance_metric'],
extend_dist_rew=v['extend_dist_rew'],
append_transformed_obs=v['append_transformed_obs'],
append_extra_info=v['append_extra_info'],
terminate_env=True,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
# Fix the variance since different goals will require different variances, making this parameter hard to learn.
learn_std=v['learn_std'],
adaptive_std=v['adaptive_std'],
std_hidden_sizes=(16,
16), # this is only used if adaptive_std is true!
output_gain=v['output_gain'],
init_std=v['policy_init_std'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
if v['baseline'] == 'g_mlp':
baseline = GaussianMLPBaseline(env_spec=env.spec)
# initialize all logging arrays on itr0
outer_iter = 0
logger.log('Generating the Initial Heatmap...')
test_and_plot_policy(policy,
env,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
limit=v['goal_range'],
center=v['goal_center'],
bounds=v['goal_range'])
# GAN
logger.log("Instantiating the GAN...")
gan_configs = {key[4:]: value for key, value in v.items() if 'GAN_' in key}
for key, value in gan_configs.items():
if value is tf.train.AdamOptimizer:
gan_configs[key] = tf.train.AdamOptimizer(gan_configs[key +
'_stepSize'])
if value is tflearn.initializations.truncated_normal:
gan_configs[key] = tflearn.initializations.truncated_normal(
stddev=gan_configs[key + '_stddev'])
gan = StateGAN(
state_size=v['goal_size'],
evaluater_size=v['num_labels'],
state_range=v['goal_range'],
state_center=v['goal_center'],
state_noise_level=v['goal_noise_level'],
generator_layers=v['gan_generator_layers'],
discriminator_layers=v['gan_discriminator_layers'],
noise_size=v['gan_noise_size'],
tf_session=tf_session,
configs=gan_configs,
)
# log first samples form the GAN
initial_goals, _ = gan.sample_states_with_noise(v['num_new_goals'])
logger.log("Labeling the goals")
labels = label_states(initial_goals,
env,
policy,
v['horizon'],
n_traj=v['n_traj'],
key='goal_reached')
plot_labeled_states(initial_goals,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'])
report.new_row()
all_goals = StateCollection(distance_threshold=v['coll_eps'])
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
feasible_goals = generate_initial_goals(env,
policy,
v['goal_range'],
goal_center=v['goal_center'],
horizon=v['horizon'])
labels = np.ones((feasible_goals.shape[0],
2)).astype(np.float32) # make them all good goals
plot_labeled_states(feasible_goals,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'],
summary_string_base='On-policy Goals:\n')
if v['only_on_policy']:
goals = feasible_goals[np.random.choice(
feasible_goals.shape[0], v['num_new_goals'], replace=False), :]
else:
logger.log("Training the GAN")
gan.pretrain(feasible_goals, v['gan_outer_iters'])
# Sample GAN
logger.log("Sampling goals from the GAN")
raw_goals, _ = gan.sample_states_with_noise(v['num_new_goals'])
if v['replay_buffer'] and outer_iter > 0 and all_goals.size > 0:
old_goals = all_goals.sample(v['num_old_goals'])
goals = np.vstack([raw_goals, old_goals])
else:
goals = raw_goals
with ExperimentLogger(log_dir,
'last',
snapshot_mode='last',
hold_outter_log=True):
logger.log("Updating the environment goal generator")
env.update_goal_generator(
UniformListStateGenerator(
goals.tolist(),
persistence=v['persistence'],
with_replacement=v['with_replacement'],
))
logger.log("Training the algorithm")
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=v['pg_batch_size'],
max_path_length=v['horizon'],
n_itr=v['inner_iters'],
step_size=0.01,
plot=False,
)
trpo_paths = algo.train()
if v['use_trpo_paths']:
logger.log("labeling starts with trpo rollouts")
[goals, labels] = label_states_from_paths(
trpo_paths,
n_traj=2,
key='goal_reached', # using the min n_traj
as_goal=True,
env=env)
paths = [path for paths in trpo_paths for path in paths]
else:
logger.log("labeling starts manually")
labels, paths = label_states(goals,
env,
policy,
v['horizon'],
as_goals=True,
n_traj=v['n_traj'],
key='goal_reached',
full_path=True)
with logger.tabular_prefix("OnStarts_"):
env.log_diagnostics(paths)
logger.log('Generating the Heatmap...')
test_and_plot_policy(policy,
env,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
limit=v['goal_range'],
center=v['goal_center'],
bounds=v['goal_range'])
plot_labeled_states(goals,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'])
logger.dump_tabular(with_prefix=False)
report.new_row()
# append new goals to list of all goals (replay buffer): Not the low reward ones!!
filtered_raw_goals = [
goal for goal, label in zip(goals, labels) if label[0] == 1
] # this is not used if no replay buffer
all_goals.append(filtered_raw_goals)
if v['add_on_policy']:
logger.log("sampling on policy")
feasible_goals = generate_initial_goals(
env,
policy,
v['goal_range'],
goal_center=v['goal_center'],
horizon=v['horizon'])
# downsampled_feasible_goals = feasible_goals[np.random.choice(feasible_goals.shape[0], v['add_on_policy']),:]
all_goals.append(feasible_goals)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('env_fname',
type=str,
help='config file with environment arguments')
parser.add_argument('transformers_fname', type=str)
parser.add_argument('mean_network_type',
type=str,
choices=['conv', 'siamese'])
parser.add_argument('--conv_filters',
nargs='*',
type=int,
default=[16, 32])
parser.add_argument('--hidden_sizes', nargs='*', type=int, default=[16])
parser.add_argument('--init_std', type=float, default=1.0)
parser.add_argument('--n_itr', type=int, default=100)
parser.add_argument('--step_size', type=float, default=0.01)
parser.add_argument('--batch_size', type=int, default=10000)
parser.add_argument('--use_static_car', action='store_true')
parser.add_argument('--use_init_heuristic', action='store_true')
args = parser.parse_args()
with open(args.env_fname) as yaml_string:
env_config = yaml.load(yaml_string)
if issubclass(env_config['class'], envs.RosEnv):
import rospy
rospy.init_node("generate_data")
env = from_config(env_config)
if args.use_static_car:
env.car_env.speed_offset_space.low = \
env.car_env.speed_offset_space.high = np.array([0.0, 4.0])
# transformers
with open(args.transformers_fname) as transformers_file:
transformers_config = yaml.load(transformers_file)
transformers = dict()
for data_name, transformer_config in transformers_config.items():
if data_name == 'action':
replace_config = {'space': env.action_space}
elif data_name in env.observation_space.spaces:
replace_config = {'space': env.observation_space.spaces[data_name]}
else:
replace_config = {}
transformers[data_name] = from_config(transformers_config[data_name],
replace_config=replace_config)
env = ServoingEnv(env)
env = RllabEnv(env, transformers=transformers)
env = normalize(env)
network_kwargs = dict(
input_shape=env.observation_space.shape,
output_dim=env.action_space.flat_dim,
conv_filters=args.conv_filters,
conv_filter_sizes=[3] * len(args.conv_filters),
conv_strides=[2] * len(args.conv_filters),
conv_pads=[0] * len(args.conv_filters),
hidden_sizes=args.hidden_sizes,
hidden_nonlinearity=LN.rectify,
output_nonlinearity=None,
name="mean_network",
)
if args.mean_network_type == 'conv':
mean_network = ConvNetwork(**network_kwargs)
elif args.mean_network_type == 'siamese':
mean_network = SiameseQuadraticErrorNetwork(**network_kwargs)
else:
raise NotImplementedError
policy = GaussianConvPolicy(
env_spec=env.spec,
init_std=args.init_std,
mean_network=mean_network,
)
if args.use_init_heuristic:
W_var = policy.get_params()[0]
W = W_var.get_value()
W[:, 3:, :, :] = -W[:, :3, :, :]
W_var.set_value(W)
baseline = GaussianConvBaseline(
env_spec=env.spec,
regressor_args=dict(
use_trust_region=True,
step_size=args.step_size,
normalize_inputs=True,
normalize_outputs=True,
hidden_sizes=args.hidden_sizes,
conv_filters=args.conv_filters,
conv_filter_sizes=[3] * len(args.conv_filters),
conv_strides=[2] * len(args.conv_filters),
conv_pads=[0] * len(args.conv_filters),
batchsize=args.batch_size * 10,
))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.batch_size,
max_path_length=100,
n_itr=args.n_itr,
discount=0.9,
step_size=args.step_size,
)
algo.train()
import IPython as ipy
ipy.embed()
def run_task(vv, log_dir=None, exp_name=None):
global policy
global baseline
trpo_stepsize = 0.01
trpo_subsample_factor = 0.2
# Check if variant is available
if vv['model_type'] not in ['BrushTireModel', 'LinearTireModel']:
raise ValueError('Unrecognized model type for simulating robot')
if vv['robot_type'] not in ['MRZR', 'RCCar']:
raise ValueError('Unrecognized robot type')
# Load environment
if not vv['use_ros']:
env = StraightEnv(target_velocity=vv['target_velocity'],
dt=vv['dt'],
model_type=vv['model_type'],
robot_type=vv['robot_type'],
mu_s=vv['mu_s'],
mu_k=vv['mu_k'])
else:
from aa_simulation.envs.straight.straight_env_ros import StraightEnvROS
env = StraightEnvROS(target_velocity=vv['target_velocity'],
dt=vv['dt'],
model_type=vv['model_type'],
robot_type=vv['robot_type'])
# Save variant information for comparison plots
variant_file = logger.get_snapshot_dir() + '/variant.json'
logger.log_variant(variant_file, vv)
# Set variance for each action component separately for exploration
# Note: We set the variance manually because we are not scaling our
# action space during training.
init_std_speed = vv['target_velocity'] / 4
init_std_steer = np.pi / 6
init_std = [init_std_speed, init_std_steer]
# Build policy and baseline networks
# Note: Mean of policy network set to analytically computed values for
# faster training (rough estimates for RL to fine-tune).
if policy is None or baseline is None:
target_velocity = vv['target_velocity']
target_steering = 0
output_mean = np.array([target_velocity, target_steering])
hidden_sizes = (32, 32)
# In mean network, allow output b values to dominate final output
# value by constraining the magnitude of the output W matrix. This is
# to allow faster learning. These numbers are arbitrarily chosen.
W_gain = min(vv['target_velocity'] / 5, np.pi / 15)
mean_network = MLP(input_shape=(env.spec.observation_space.flat_dim, ),
output_dim=env.spec.action_space.flat_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=LN.tanh,
output_nonlinearity=None,
output_W_init=LI.GlorotUniform(gain=W_gain),
output_b_init=output_mean)
policy = GaussianMLPPolicy(env_spec=env.spec,
hidden_sizes=(32, 32),
init_std=init_std,
mean_network=mean_network)
baseline = LinearFeatureBaseline(env_spec=env.spec,
target_key='returns')
# Reset variance to re-enable exploration when using pre-trained networks
else:
policy._l_log_std = ParamLayer(
policy._mean_network.input_layer,
num_units=env.spec.action_space.flat_dim,
param=LI.Constant(np.log(init_std)),
name='output_log_std',
trainable=True)
obs_var = policy._mean_network.input_layer.input_var
mean_var, log_std_var = L.get_output(
[policy._l_mean, policy._l_log_std])
policy._log_std_var = log_std_var
LasagnePowered.__init__(policy, [policy._l_mean, policy._l_log_std])
policy._f_dist = ext.compile_function(inputs=[obs_var],
outputs=[mean_var, log_std_var])
safety_baseline = LinearFeatureBaseline(env_spec=env.spec,
target_key='safety_returns')
safety_constraint = StraightSafetyConstraint(max_value=1.0,
baseline=safety_baseline)
if vv['algo'] == 'TRPO':
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=600,
max_path_length=env.horizon,
n_itr=600,
discount=0.99,
step_size=trpo_stepsize,
plot=False,
)
else:
algo = CPO(env=env,
policy=policy,
baseline=baseline,
safety_constraint=safety_constraint,
batch_size=600,
max_path_length=env.horizon,
n_itr=600,
discount=0.99,
step_size=trpo_stepsize,
gae_lambda=0.95,
safety_gae_lambda=1,
optimizer_args={'subsample_factor': trpo_subsample_factor},
plot=False)
algo.train()
mdp = NormalizedEnv(CartpoleSwingupEnvX())
for seed in seeds:
policy = GaussianMLPPolicy(
env_spec=mdp.spec,
hidden_sizes=(64, 32),
)
baseline = LinearFeatureBaseline(mdp.spec, )
batch_size = 50000
algo = TRPO(
env=mdp,
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=500,
n_itr=10000,
step_size=0.01,
subsample_factor=1.0,
)
run_experiment_lite(algo.train(),
exp_prefix="trpo",
n_parallel=4,
snapshot_mode="last",
seed=seed,
mode="local")
def run_task(_):
"""Implement the run_task method needed to run experiments with rllab."""
sumo_params = SumoParams(sumo_binary="sumo-gui",
sim_step=0.2,
restart_instance=True)
# RL vehicles constitute 5% of the total number of vehicles
vehicles = Vehicles()
vehicles.add(veh_id="human",
acceleration_controller=(IDMController, {
"noise": 0.2
}),
speed_mode="no_collide",
num_vehicles=5)
vehicles.add(veh_id="rl",
acceleration_controller=(RLController, {}),
speed_mode="no_collide",
num_vehicles=0)
# Vehicles are introduced from both sides of merge, with RL vehicles
# entering from the highway portion as well
inflow = InFlows()
inflow.add(veh_type="human",
edge="inflow_highway",
vehs_per_hour=(1 - RL_PENETRATION) * FLOW_RATE,
departLane="free",
departSpeed=10)
inflow.add(veh_type="rl",
edge="inflow_highway",
vehs_per_hour=RL_PENETRATION * FLOW_RATE,
departLane="free",
departSpeed=10)
inflow.add(veh_type="human",
edge="inflow_merge",
vehs_per_hour=100,
departLane="free",
departSpeed=7.5)
additional_env_params = {
"target_velocity": 25,
"num_rl": NUM_RL,
"max_accel": 1.5,
"max_decel": 1.5
}
env_params = EnvParams(horizon=HORIZON,
sims_per_step=5,
warmup_steps=0,
additional_params=additional_env_params)
additional_net_params = ADDITIONAL_NET_PARAMS.copy()
additional_net_params["merge_lanes"] = 1
additional_net_params["highway_lanes"] = 1
additional_net_params["pre_merge_length"] = 500
net_params = NetParams(in_flows=inflow,
no_internal_links=False,
additional_params=additional_net_params)
initial_config = InitialConfig(spacing="uniform",
lanes_distribution=float("inf"))
scenario = MergeScenario(name="merge-rl",
generator_class=MergeGenerator,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env_name = "WaveAttenuationMergePOEnv"
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)
env = normalize(env)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32, 32),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=HORIZON * N_ROLLOUTS,
max_path_length=HORIZON,
n_itr=1000,
# whole_paths=True,
discount=0.999,
)
algo.train(),
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
# Log performance of randomly initialized policy with FIXED goal [0.1, 0.1]
logger.log("Initializing report...")
log_dir = logger.get_snapshot_dir() # problem with logger module here!!
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=4)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
inner_env = normalize(Arm3dKeyEnv(ctrl_cost_coeff=v['ctrl_cost_coeff']))
fixed_goal_generator = FixedStateGenerator(state=v['ultimate_goal'])
fixed_start_generator = FixedStateGenerator(state=v['start_goal'])
env = GoalStartExplorationEnv(
env=inner_env,
start_generator=fixed_start_generator,
obs2start_transform=lambda x: x[:v['start_size']],
goal_generator=fixed_goal_generator,
obs2goal_transform=lambda x: x[-1 * v['goal_size']:
], # the goal are the last 9 coords
terminal_eps=v['terminal_eps'],
distance_metric=v['distance_metric'],
extend_dist_rew=v['extend_dist_rew'],
inner_weight=v['inner_weight'],
goal_weight=v['goal_weight'],
terminate_env=True,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=v['policy_hidden_sizes'],
# Fix the variance since different goals will require different variances, making this parameter hard to learn.
learn_std=v['learn_std'],
adaptive_std=v['adaptive_std'],
std_hidden_sizes=(16,
16), # this is only used if adaptive_std is true!
output_gain=v['output_gain'],
init_std=v['policy_init_std'],
)
if v['baseline'] == 'linear':
baseline = LinearFeatureBaseline(env_spec=env.spec)
elif v['baseline'] == 'g_mlp':
baseline = GaussianMLPBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=v['pg_batch_size'],
max_path_length=v['horizon'],
n_itr=v['inner_iters'],
step_size=0.01,
discount=v['discount'],
plot=False,
)
# load the state collection from data_upload
load_dir = 'data_upload/state_collections/'
all_feasible_starts = pickle.load(
open(
osp.join(config.PROJECT_PATH, load_dir, 'all_feasible_states.pkl'),
'rb'))
# all_feasible_starts = pickle.load(
# open(osp.join(config.PROJECT_PATH, load_dir, 'key_all_feasible_04_230000.pkl'), 'rb'))
# all_feasible_starts = pickle.load(
# open(osp.join(config.PROJECT_PATH, load_dir, 'key_all_feasible_states_med_rad4.pkl'), 'rb'))
all_feasible_starts2 = pickle.load(
open(
osp.join(config.PROJECT_PATH, load_dir,
'key_all_feasible_states_min_rad4.pkl'), 'rb'))
all_feasible_starts3 = pickle.load(
open(
osp.join(config.PROJECT_PATH, load_dir,
'key_all_feasible_states_max_rad2.pkl'), 'rb'))
print("we have %d feasible starts" % all_feasible_starts.size)
all_starts = StateCollection(distance_threshold=v['coll_eps'])
brownian_starts = StateCollection(
distance_threshold=v['regularize_starts'])
logger.log(
'Generating seed starts from the goal (horizon 10, subsample 600 of them)'
)
with algo.env.set_kill_outside(radius=v['kill_radius']):
seed_starts = generate_starts(
env,
starts=[v['start_goal']],
horizon=10, # this is smaller as they are seeds!
variance=v['brownian_variance'],
subsample=v['num_new_starts']) # , animated=True, speedup=10)
# seed_starts = all_feasible_starts.states
# with env.set_kill_outside(radius=0.4):
# find_all_feasible_states(env, seed_starts, distance_threshold=0.1, brownian_variance=1, animate=False)
# # show where these states are:
# shuffled_starts = np.array(all_feasible_starts.state_list)
# np.random.shuffle(shuffled_starts)
# generate_starts(env, starts=shuffled_starts, horizon=100, variance=v['brownian_variance'],
# zero_action=True, animated=True, speedup=10)
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
logger.log("Sampling starts")
with algo.env.set_kill_outside(radius=v['kill_radius']):
starts = generate_starts(algo.env,
starts=seed_starts,
horizon=v['brownian_horizon'],
variance=v['brownian_variance'])
# regularization of the brownian starts
brownian_starts.empty()
brownian_starts.append(starts)
starts = brownian_starts.sample(size=v['num_new_starts'])
if v['replay_buffer'] and outer_iter > 0 and all_starts.size > 0:
old_starts = all_starts.sample(v['num_old_starts'])
starts = np.vstack([starts, old_starts])
with ExperimentLogger(log_dir,
50 * (outer_iter // 50 + 1),
snapshot_mode='last',
hold_outter_log=True):
logger.log("Updating the environment start generator")
algo.env.update_start_generator(
UniformListStateGenerator(
starts.tolist(),
persistence=v['persistence'],
with_replacement=v['with_replacement'],
))
# algo.start_worker()
logger.log("Training the algorithm")
algo.current_itr = 0
trpo_paths = algo.train(already_init=outer_iter > 1)
# import pdb; pdb.set_trace()
if v['use_trpo_paths']:
logger.log("labeling starts with trpo rollouts")
[starts, labels] = label_states_from_paths(
trpo_paths,
n_traj=2,
key='goal_reached', # using the min n_traj
as_goal=False,
env=algo.env)
paths = [path for paths in trpo_paths for path in paths]
else:
logger.log("labeling starts manually")
labels, paths = label_states(starts,
algo.env,
policy,
v['horizon'],
as_goals=False,
n_traj=v['n_traj'],
key='goal_reached',
full_path=True)
with logger.tabular_prefix("OnStarts_"):
algo.env.log_diagnostics(paths)
logger.record_tabular('brownian_starts', brownian_starts.size)
start_classes, text_labels = convert_label(labels)
total_starts = labels.shape[0]
logger.record_tabular('GenStarts_evaluated', total_starts)
start_class_frac = OrderedDict(
) # this needs to be an ordered dict!! (for the log tabular)
for k in text_labels.keys():
frac = np.sum(start_classes == k) / total_starts
logger.record_tabular('GenStart_frac_' + text_labels[k], frac)
start_class_frac[text_labels[k]] = frac
labels = np.logical_and(labels[:, 0],
labels[:, 1]).astype(int).reshape((-1, 1))
logger.log("Labeling on uniform starts")
with logger.tabular_prefix("Uniform_4med_"):
unif_starts = all_feasible_starts.sample(500)
unif_starts = np.pad(unif_starts,
((0, v['start_size'] - unif_starts.shape[1])),
'constant')
mean_reward, paths = evaluate_states(unif_starts,
algo.env,
policy,
v['horizon'],
n_traj=1,
key='goal_reached',
as_goals=False,
full_path=True)
algo.env.log_diagnostics(paths)
# with logger.tabular_prefix("Uniform_4med_bis_"):
# unif_starts = all_feasible_starts.sample(200)
# unif_starts1bis = np.pad(unif_starts, ((0, v['start_size'] - unif_starts.shape[1])), 'constant')
# mean_reward1bis, paths1bis = evaluate_states(unif_starts1bis, algo.env, policy, v['horizon'], n_traj=1,
# key='goal_reached', as_goals=False, full_path=True)
# algo.env.log_diagnostics(paths1bis)
# with logger.tabular_prefix("Uniform_4min_"):
# unif_starts2 = all_feasible_starts2.sample(200)
# unif_starts2 = np.pad(unif_starts2, ((0, v['start_size'] - unif_starts2.shape[1])), 'constant')
# mean_reward2, paths2 = evaluate_states(unif_starts2, algo.env, policy, v['horizon'], n_traj=1,
# key='goal_reached', as_goals=False, full_path=True)
# algo.env.log_diagnostics(paths2)
# with logger.tabular_prefix("Uniform_2max_"):
# unif_starts3 = all_feasible_starts3.sample(200)
# unif_starts3 = np.pad(unif_starts3, ((0, v['start_size'] - unif_starts3.shape[1])), 'constant')
# mean_reward3, paths3 = evaluate_states(unif_starts3, algo.env, policy, v['horizon'], n_traj=1,
# key='goal_reached', as_goals=False, full_path=True)
# algo.env.log_diagnostics(paths3)
logger.dump_tabular(with_prefix=True)
# append new states to list of all starts (replay buffer):
logger.log("Appending good goals to replay and generating seeds")
filtered_raw_starts = [
start for start, label in zip(starts, labels) if label[0] == 1
]
all_starts.append(filtered_raw_starts)
if v['seed_with'] == 'only_goods':
if len(filtered_raw_starts) > 0:
seed_starts = filtered_raw_starts
elif np.sum(start_classes == 0) > np.sum(
start_classes == 1): # if more low reward than high reward
seed_starts = all_starts.sample(
300) # sample them from the replay
else: # add a tone of noise if all the states I had ended up being high_reward!
with algo.env.set_kill_outside(radius=v['kill_radius']):
seed_starts = generate_starts(
algo.env,
starts=starts,
horizon=int(v['horizon'] * 10),
subsample=v['num_new_starts'],
variance=v['brownian_variance'] * 10)
elif v['seed_with'] == 'all_previous':
all_starts.append(starts)
seed_starts = starts
elif v['seed_with'] == 'on_policy':
with algo.env.set_kill_outside(radius=v['kill_radius']):
seed_starts = generate_starts(algo.env,
policy,
horizon=v['horizon'],
subsample=v['num_new_starts'])
hidden_sizes=(42, 42))
baseline = LinearFeatureBaseline(env_spec=env.spec)
vg = instrument.VariantGenerator()
vg.add("seed", [1, 2, 3, 4, 5])
variants = vg.variants()
for variant in variants:
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=90000,
max_path_length=100,
n_itr=100,
discount=0.99,
step_size=0.1,
optimizer_args={'cg_iters': 100},
plot=True,
)
run_experiment_lite(
algo.train(),
# Number of parallel workers for sampling
n_parallel=8,
plot=True,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used,
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
sampling_res = 2 if 'sampling_res' not in v.keys() else v['sampling_res']
# Log performance of randomly initialized policy with FIXED goal [0.1, 0.1]
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir() # problem with logger module here!!
if log_dir is None:
log_dir = "/home/davheld/repos/rllab_goal_rl/data/local/debug"
debug = True
else:
debug = False
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=5)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
inner_env = normalize(PointMazeEnv(maze_id=v['maze_id']))
uniform_goal_generator = UniformStateGenerator(state_size=v['goal_size'],
bounds=v['goal_range'],
center=v['goal_center'])
env = GoalExplorationEnv(
env=inner_env,
goal_generator=uniform_goal_generator,
#obs2goal_transform=lambda x: x[:int(len(x) / 2)],
obs2goal_transform=lambda x: x[:v['goal_size']],
terminal_eps=v['terminal_eps'],
distance_metric=v['distance_metric'],
extend_dist_rew=v['extend_dist_rew'],
only_feasible=v['only_feasible'],
goal_weight=v['goal_weight'],
terminate_env=True,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
# Fix the variance since different goals will require different variances, making this parameter hard to learn.
learn_std=v['learn_std'],
adaptive_std=v['adaptive_std'],
std_hidden_sizes=(16,
16), # this is only used if adaptive_std is true!
output_gain=v['output_gain'],
init_std=v['policy_init_std'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
outer_iter = 0
if not debug and not v['fast_mode']:
logger.log('Generating the Initial Heatmap...')
test_and_plot_policy(policy,
env,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
limit=v['goal_range'],
center=v['goal_center'])
report.new_row()
sagg_riac = SaggRIAC(state_size=v['goal_size'],
state_range=v['goal_range'],
state_center=v['goal_center'],
max_goals=v['max_goals'],
max_history=v['max_history'])
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
raw_goals = sagg_riac.sample_states(num_samples=v['num_new_goals'])
goals = raw_goals
with ExperimentLogger(log_dir,
'last',
snapshot_mode='last',
hold_outter_log=True):
logger.log("Updating the environment goal generator")
env.update_goal_generator(
UniformListStateGenerator(
goals,
persistence=v['persistence'],
with_replacement=v['with_replacement'],
))
logger.log("Training the algorithm")
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=v['pg_batch_size'],
max_path_length=v['horizon'],
n_itr=v['inner_iters'],
step_size=0.01,
discount=v['discount'],
plot=False,
)
all_paths = algo.train()
if v['use_competence_ratio']:
[goals, rewards
] = compute_rewards_from_paths(all_paths,
key='competence',
as_goal=True,
env=env,
terminal_eps=v['terminal_eps'])
else:
[goals, rewards] = compute_rewards_from_paths(all_paths,
key='rewards',
as_goal=True,
env=env)
[goals_with_labels,
labels] = label_states_from_paths(all_paths,
n_traj=v['n_traj'],
key='goal_reached')
plot_labeled_states(goals_with_labels,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'],
maze_id=v['maze_id'])
logger.log('Generating the Heatmap...')
test_and_plot_policy(policy,
env,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
limit=v['goal_range'],
center=v['goal_center'])
sagg_riac.plot_regions_interest(maze_id=v['maze_id'], report=report)
sagg_riac.plot_regions_states(maze_id=v['maze_id'], report=report)
logger.log("Updating SAGG-RIAC")
sagg_riac.add_states(goals, rewards)
# Find final states "accidentally" reached by the agent.
final_goals = compute_final_states_from_paths(all_paths,
as_goal=True,
env=env)
sagg_riac.add_accidental_states(final_goals, v['extend_dist_rew'])
logger.dump_tabular(with_prefix=False)
report.new_row()
# Created by Xingyu Lin, 10/06/2018
from rllab.algos.trpo import TRPO
from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
from envs.square2d.square2d_nongoal import Square2dEnv
from rllab.envs.normalized_env import normalize
from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
from rllab.misc.instrument import run_experiment_lite
env = normalize(Square2dEnv())
policy = GaussianMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=1000,
discount=0.99,
step_size=0.01,
)
algo.train()
env = normalize(CartpoleEnv())
policy = GaussianMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=40,
discount=0.99,
step_size=step_size,
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
run_experiment_lite(
algo.train(),
exp_prefix="first_exp",
# Number of parallel workers for sampling
n_parallel=1,
# Only keep the snapshot parameters for the last iteration
snapshot_mode="last",
# Specifies the seed for the experiment. If this is not provided, a random seed
# will be used
stub(globals())
# Param ranges
seeds = range(2)
# SwimmerGather hierarchical task
mdp_classes = [SwimmerGatherEnv]
mdps = [NormalizedEnv(env=mdp_class()) for mdp_class in mdp_classes]
param_cart_product = itertools.product(mdps, seeds)
for mdp, seed in param_cart_product:
policy = GaussianMLPPolicy(env_spec=mdp.spec, hidden_sizes=(64, 32))
baseline = LinearFeatureBaseline(mdp.spec)
batch_size = 50000
algo = TRPO(
env=mdp,
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=500,
n_itr=10000,
step_size=0.01,
subsample_factor=1.0,
)
run_experiment_lite(algo.train(), exp_prefix="trpo", n_parallel=4, snapshot_mode="last", seed=seed, mode="local")
"mean_network": None,
"hidden_sizes": (100, 50, 25),
"hidden_nonlinearity": NL.tanh,
"optimizer": base_line_optimizer,
"use_trust_region": True,
"step_size": 0.01,
"learn_std": True,
"init_std": 1.0,
"adaptive_std": False,
"std_share_network": False,
"std_hidden_sizes": (32, 32),
"std_nonlinearity": None,
"normalize_inputs": True,
"normalize_outputs": True,
})
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
n_itr=total_iter,
max_path_length=max_path_length,
experiment_spec=experiment_spec,
save_policy_every=save_policy_every,
batch_size=batch_size,
discount=0.995,
gae_lambda=0.98,
)
algo.train(),
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 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=0.99)
parser.add_argument('--gae_lambda', type=float, default=1.0)
parser.add_argument('--reward_scale', type=float, default=1.0)
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('--rectangle', type=str, default='10,10')
parser.add_argument('--map_type', type=str, default='rectangle')
parser.add_argument('--n_evaders', type=int, default=5)
parser.add_argument('--n_pursuers', type=int, default=2)
parser.add_argument('--obs_range', type=int, default=3)
parser.add_argument('--n_catch', type=int, default=2)
parser.add_argument('--urgency', type=float, default=0.0)
parser.add_argument('--pursuit', dest='train_pursuit', action='store_true')
parser.add_argument('--evade', dest='train_pursuit', action='store_false')
parser.set_defaults(train_pursuit=True)
parser.add_argument('--surround', action='store_true', default=False)
parser.add_argument('--constraint_window', type=float, default=1.0)
parser.add_argument('--sample_maps', action='store_true', default=False)
parser.add_argument('--map_file', type=str, default='../maps/map_pool.npy')
parser.add_argument('--flatten', action='store_true', default=False)
parser.add_argument('--reward_mech', type=str, default='global')
parser.add_argument('--catchr', type=float, default=0.1)
parser.add_argument('--term_pursuit', type=float, default=5.0)
parser.add_argument('--recurrent', type=str, default=None)
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('--baseline_type', type=str, default='linear')
parser.add_argument('--conv', action='store_true', default=False)
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(',')))
if args.sample_maps:
map_pool = np.load(args.map_file)
else:
if args.map_type == 'rectangle':
env_map = TwoDMaps.rectangle_map(*map(int, args.rectangle.split(',')))
elif args.map_type == 'complex':
env_map = TwoDMaps.complex_map(*map(int, args.rectangle.split(',')))
else:
raise NotImplementedError()
map_pool = [env_map]
env = PursuitEvade(map_pool, n_evaders=args.n_evaders, n_pursuers=args.n_pursuers,
obs_range=args.obs_range, n_catch=args.n_catch,
train_pursuit=args.train_pursuit, urgency_reward=args.urgency,
surround=args.surround, sample_maps=args.sample_maps,
constraint_window=args.constraint_window,
flatten=args.flatten,
reward_mech=args.reward_mech,
catchr=args.catchr,
term_pursuit=args.term_pursuit)
env = RLLabEnv(
StandardizedEnv(env, scale_reward=args.reward_scale, enable_obsnorm=False),
mode=args.control)
if args.recurrent:
if args.conv:
feature_network = ConvNetwork(
input_shape=emv.spec.observation_space.shape,
output_dim=5,
conv_filters=(8,16,16),
conv_filter_sizes=(3,3,3),
conv_strides=(1,1,1),
conv_pads=('VALID','VALID','VALID'),
hidden_sizes=(64,),
hidden_nonlinearity=NL.rectify,
output_nonlinearity=NL.softmax)
else:
feature_network = MLP(
input_shape=(env.spec.observation_space.flat_dim + env.spec.action_space.flat_dim,),
output_dim=5, hidden_sizes=(128,128,128), hidden_nonlinearity=NL.tanh,
output_nonlinearity=None)
if args.recurrent == 'gru':
policy = CategoricalGRUPolicy(env_spec=env.spec, feature_network=feature_network,
hidden_dim=int(args.policy_hidden_sizes))
elif args.conv:
feature_network = ConvNetwork(
input_shape=env.spec.observation_space.shape,
output_dim=5,
conv_filters=(8,16,16),
conv_filter_sizes=(3,3,3),
conv_strides=(1,1,1),
conv_pads=('valid','valid','valid'),
hidden_sizes=(64,),
hidden_nonlinearity=NL.rectify,
output_nonlinearity=NL.softmax)
policy = CategoricalMLPPolicy(env_spec=env.spec, prob_network=feature_network)
else:
policy = CategoricalMLPPolicy(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,
gae_lambda=args.gae_lambda,
step_size=args.max_kl,
mode=args.control,)
algo.train()
def run_task(*_):
"""Implement the run_task method needed to run experiments with rllab."""
v_enter = 10
inner_length = 300
long_length = 100
short_length = 300
n = 3
m = 3
num_cars_left = 1
num_cars_right = 1
num_cars_top = 1
num_cars_bot = 1
tot_cars = (num_cars_left + num_cars_right) * m \
+ (num_cars_bot + num_cars_top) * n
grid_array = {
"short_length": short_length,
"inner_length": inner_length,
"long_length": long_length,
"row_num": n,
"col_num": m,
"cars_left": num_cars_left,
"cars_right": num_cars_right,
"cars_top": num_cars_top,
"cars_bot": num_cars_bot
}
sumo_params = SumoParams(sim_step=1, render=True)
vehicles = Vehicles()
vehicles.add(veh_id="idm",
acceleration_controller=(SumoCarFollowingController, {}),
sumo_car_following_params=SumoCarFollowingParams(
min_gap=2.5, tau=1.1, max_speed=v_enter),
routing_controller=(GridRouter, {}),
num_vehicles=tot_cars,
speed_mode="all_checks")
tl_logic = TrafficLights(baseline=False)
additional_env_params = {
"target_velocity": 50,
"switch_time": 3.0,
"num_observed": 2,
"discrete": False,
"tl_type": "controlled"
}
env_params = EnvParams(additional_params=additional_env_params)
additional_net_params = {
"speed_limit": 35,
"grid_array": grid_array,
"horizontal_lanes": 1,
"vertical_lanes": 1
}
initial_config, net_params = get_flow_params(10, 300, n, m,
additional_net_params)
scenario = SimpleGridScenario(name="grid-intersection",
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config,
traffic_lights=tl_logic)
env_name = "PO_TrafficLightGridEnv"
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 = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=40000,
max_path_length=horizon,
# whole_paths=True,
n_itr=800,
discount=0.999,
# step_size=0.01,
)
algo.train()
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
if DEBUG:
n_itr = 5
else:
n_itr = config.num_iter
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=config.batch_size,
max_path_length=env.horizon,
n_itr=n_itr,
discount=config.discount,
step_size=config.step_size,
gae_lambda=config.gae_lambda,
num_workers=config.num_workers,
plot_learning_curve=config.plot_learning_curve,
trial=agent_num,
)
avg_rewards, std_rewards = algo.train()
print("training completed!")
saveModel(algo.policy,
'policy_{}_config_{}_agent_{}'.format(dynamic_environments[args.env_ind], args.config_num, agent_num))
# save rewards per model over the iterations
# also plot the rewards
if config.plot_learning_curve:
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
sampling_res = 2 if 'sampling_res' not in v.keys() else v['sampling_res']
# Log performance of randomly initialized policy with FIXED goal [0.1, 0.1]
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir() # problem with logger module here!!
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=4)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
tf_session = tf.Session()
inner_env = normalize(PointMazeEnv(maze_id=v['maze_id']))
fixed_goal_generator = FixedStateGenerator(state=v['ultimate_goal'])
uniform_start_generator = UniformStateGenerator(state_size=v['start_size'],
bounds=v['start_range'],
center=v['start_center'])
env = GoalStartExplorationEnv(
env=inner_env,
start_generator=uniform_start_generator,
obs2start_transform=lambda x: x[:v['start_size']],
goal_generator=fixed_goal_generator,
obs2goal_transform=lambda x: x[:v['goal_size']],
terminal_eps=v['terminal_eps'],
distance_metric=v['distance_metric'],
extend_dist_rew=v['extend_dist_rew'],
only_feasible=v['only_feasible'],
terminate_env=True,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
# Fix the variance since different goals will require different variances, making this parameter hard to learn.
learn_std=v['learn_std'],
adaptive_std=v['adaptive_std'],
std_hidden_sizes=(16,
16), # this is only used if adaptive_std is true!
output_gain=v['output_gain'],
init_std=v['policy_init_std'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
# initialize all logging arrays on itr0
outer_iter = 0
logger.log('Generating the Initial Heatmap...')
plot_policy_means(policy,
env,
sampling_res=2,
report=report,
limit=v['start_range'],
center=v['start_center'])
# test_and_plot_policy(policy, env, as_goals=False, max_reward=v['max_reward'], sampling_res=sampling_res, n_traj=v['n_traj'],
# itr=outer_iter, report=report, limit=v['goal_range'], center=v['goal_center'])
# GAN
logger.log("Instantiating the GAN...")
gan_configs = {key[4:]: value for key, value in v.items() if 'GAN_' in key}
for key, value in gan_configs.items():
if value is tf.train.AdamOptimizer:
gan_configs[key] = tf.train.AdamOptimizer(gan_configs[key +
'_stepSize'])
if value is tflearn.initializations.truncated_normal:
gan_configs[key] = tflearn.initializations.truncated_normal(
stddev=gan_configs[key + '_stddev'])
gan = StateGAN(
state_size=v['start_size'],
evaluater_size=v['num_labels'],
state_range=v['start_range'],
state_center=v['start_center'],
state_noise_level=v['start_noise_level'],
generator_layers=v['gan_generator_layers'],
discriminator_layers=v['gan_discriminator_layers'],
noise_size=v['gan_noise_size'],
tf_session=tf_session,
configs=gan_configs,
)
logger.log("pretraining the GAN...")
if v['smart_init']:
feasible_starts = generate_starts(
env, starts=[v['ultimate_goal']],
horizon=50) # without giving the policy it does brownian mo.
labels = np.ones((feasible_starts.shape[0],
2)).astype(np.float32) # make them all good goals
plot_labeled_states(feasible_starts,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'],
maze_id=v['maze_id'])
dis_loss, gen_loss = gan.pretrain(states=feasible_starts,
outer_iters=v['gan_outer_iters'])
print("Loss of Gen and Dis: ", gen_loss, dis_loss)
else:
gan.pretrain_uniform(outer_iters=500,
report=report) # v['gan_outer_iters'])
# log first samples form the GAN
initial_starts, _ = gan.sample_states_with_noise(v['num_new_starts'])
logger.log("Labeling the starts")
labels = label_states(initial_starts,
env,
policy,
v['horizon'],
as_goals=False,
n_traj=v['n_traj'],
key='goal_reached')
plot_labeled_states(initial_starts,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'],
maze_id=v['maze_id'])
report.new_row()
all_starts = StateCollection(distance_threshold=v['coll_eps'])
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
# Sample GAN
logger.log("Sampling starts from the GAN")
raw_starts, _ = gan.sample_states_with_noise(v['num_new_starts'])
if v['replay_buffer'] and outer_iter > 0 and all_starts.size > 0:
old_starts = all_starts.sample(v['num_old_starts'])
starts = np.vstack([raw_starts, old_starts])
else:
starts = raw_starts
with ExperimentLogger(log_dir,
'last',
snapshot_mode='last',
hold_outter_log=True):
logger.log("Updating the environment start generator")
env.update_start_generator(
UniformListStateGenerator(
starts.tolist(),
persistence=v['persistence'],
with_replacement=v['with_replacement'],
))
logger.log("Training the algorithm")
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=v['pg_batch_size'],
max_path_length=v['horizon'],
n_itr=v['inner_iters'],
step_size=0.01,
discount=v['discount'],
plot=False,
)
trpo_paths = algo.train()
if v['use_trpo_paths']:
logger.log("labeling starts with trpo rollouts")
[starts, labels] = label_states_from_paths(
trpo_paths,
n_traj=2,
key='goal_reached', # using the min n_traj
as_goal=False,
env=env)
paths = [path for paths in trpo_paths for path in paths]
else:
logger.log("labeling starts manually")
labels, paths = label_states(starts,
env,
policy,
v['horizon'],
as_goals=False,
n_traj=v['n_traj'],
key='goal_reached',
full_path=True)
with logger.tabular_prefix("OnStarts_"):
env.log_diagnostics(paths)
plot_labeled_states(starts,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'],
maze_id=v['maze_id'])
logger.log('Generating the Heatmap...')
plot_policy_means(policy,
env,
sampling_res=2,
report=report,
limit=v['start_range'],
center=v['start_center'])
test_and_plot_policy(policy,
env,
as_goals=False,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
limit=v['goal_range'],
center=v['goal_center'])
# ###### extra for deterministic:
# logger.log("Labeling the goals deterministic")
# with policy.set_std_to_0():
# labels_det = label_states(goals, env, policy, v['horizon'], n_traj=v['n_traj'], n_processes=1)
# plot_labeled_states(goals, labels_det, report=report, itr=outer_iter, limit=v['goal_range'], center=v['goal_center'])
labels = np.logical_and(labels[:, 0],
labels[:, 1]).astype(int).reshape((-1, 1))
logger.log("Training the GAN")
if np.any(labels):
gan.train(
starts,
labels,
v['gan_outer_iters'],
)
logger.dump_tabular(with_prefix=False)
report.new_row()
# append new goals to list of all goals (replay buffer): Not the low reward ones!!
filtered_raw_start = [
start for start, label in zip(starts, labels) if label[0] == 1
]
all_starts.append(filtered_raw_start)
def train(num_experiments, thread_id, queue):
############ DEFAULT PARAMETERS ############
env_name = None #Name of adversarial environment
path_length = 1000 #Maximum episode length
layer_size = tuple([100, 100, 100]) #Layer definition
ifRender = False #Should we render?
afterRender = 100 #After how many to animate
n_exps = 1 #Number of training instances to run
n_itr = 25 #Number of iterations of the alternating optimization
n_pro_itr = 1 #Number of iterations for the protaginist
n_adv_itr = 1 #Number of interations for the adversary
batch_size = 4000 #Number of training samples for each iteration
ifSave = True #Should we save?
save_every = 100 #Save checkpoint every save_every iterations
n_process = 1 #Number of parallel threads for sampling environment
adv_fraction = 0.25 #Fraction of maximum adversarial force to be applied
step_size = 0.01 #kl step size for TRPO
gae_lambda = 0.97 #gae_lambda for learner
save_dir = './results' #folder to save result in
############ ENV SPECIFIC PARAMETERS ############
env_name = 'Walker2dAdv-v1'
layer_size = tuple([64, 64])
step_size = 0.1
gae_lambda = 0.97
batch_size = 25000
n_exps = num_experiments
n_itr = 500
ifSave = False
n_process = 4
adv_fraction = 5.0
save_dir = './../results/StaticWalker'
args = [
env_name, path_length, layer_size, ifRender, afterRender, n_exps,
n_itr, n_pro_itr, n_adv_itr, batch_size, save_every, n_process,
adv_fraction, step_size, gae_lambda, save_dir
]
############ ADVERSARIAL POLICY LOAD ############
filepath = './../initial_results/Walker/env-Walker2dAdv-v1_Exp1_Itr1500_BS25000_Adv0.25_stp0.01_lam0.97_507500.p'
res_D = pickle.load(open(filepath, 'rb'))
pretrained_adv_policy = res_D['adv_policy']
############ MAIN LOOP ############
## Initializing summaries for the tests ##
const_test_rew_summary = []
rand_test_rew_summary = []
step_test_rew_summary = []
rand_step_test_rew_summary = []
adv_test_rew_summary = []
## Preparing file to save results in ##
save_prefix = 'static_env-{}_Exp{}_Itr{}_BS{}_Adv{}_stp{}_lam{}_{}'.format(
env_name, n_exps, n_itr, batch_size, adv_fraction, step_size,
gae_lambda, random.randint(0, 1000000))
save_name = save_dir + '/' + save_prefix
## Looping over experiments to carry out ##
for ne in range(n_exps):
## Environment definition ##
## The second argument in GymEnv defines the relative magnitude of adversary. For testing we set this to 1.0.
env = normalize(GymEnv(env_name, adv_fraction))
env_orig = normalize(GymEnv(env_name, 1.0))
## Protagonist policy definition ##
pro_policy = GaussianMLPPolicy(env_spec=env.spec,
hidden_sizes=layer_size,
is_protagonist=True)
pro_baseline = LinearFeatureBaseline(env_spec=env.spec)
## Zero Adversary for the protagonist training ##
zero_adv_policy = ConstantControlPolicy(env_spec=env.spec,
is_protagonist=False,
constant_val=0.0)
## Adversary policy definition ##
adv_policy = pretrained_adv_policy
adv_baseline = LinearFeatureBaseline(env_spec=env.spec)
## Initializing the parallel sampler ##
parallel_sampler.initialize(n_process)
## Optimizer for the Protagonist ##
pro_algo = TRPO(env=env,
pro_policy=pro_policy,
adv_policy=adv_policy,
pro_baseline=pro_baseline,
adv_baseline=adv_baseline,
batch_size=batch_size,
max_path_length=path_length,
n_itr=n_pro_itr,
discount=0.995,
gae_lambda=gae_lambda,
step_size=step_size,
is_protagonist=True)
## Setting up summaries for testing for a specific training instance ##
pro_rews = []
adv_rews = []
all_rews = []
const_testing_rews = []
const_testing_rews.append(
test_const_adv(env_orig, pro_policy, path_length=path_length))
rand_testing_rews = []
rand_testing_rews.append(
test_rand_adv(env_orig, pro_policy, path_length=path_length))
step_testing_rews = []
step_testing_rews.append(
test_step_adv(env_orig, pro_policy, path_length=path_length))
rand_step_testing_rews = []
rand_step_testing_rews.append(
test_rand_step_adv(env_orig, pro_policy, path_length=path_length))
adv_testing_rews = []
adv_testing_rews.append(
test_learnt_adv(env,
pro_policy,
adv_policy,
path_length=path_length))
## Beginning alternating optimization ##
for ni in range(n_itr):
logger.log('\n\nThread: {} Experiment: {} Iteration: {}\n'.format(
thread_id,
ne,
ni,
))
## Train Protagonist
pro_algo.train()
pro_rews += pro_algo.rews
all_rews += pro_algo.rews
logger.log('Protag Reward: {}'.format(
np.array(pro_algo.rews).mean()))
## Test the learnt policies
const_testing_rews.append(
test_const_adv(env, pro_policy, path_length=path_length))
rand_testing_rews.append(
test_rand_adv(env, pro_policy, path_length=path_length))
step_testing_rews.append(
test_step_adv(env, pro_policy, path_length=path_length))
rand_step_testing_rews.append(
test_rand_step_adv(env, pro_policy, path_length=path_length))
adv_testing_rews.append(
test_learnt_adv(env,
pro_policy,
adv_policy,
path_length=path_length))
if ni % afterRender == 0 and ifRender == True:
test_const_adv(env,
pro_policy,
path_length=path_length,
n_traj=1,
render=True)
if ni != 0 and ni % save_every == 0 and ifSave == True:
## SAVING CHECKPOINT INFO ##
pickle.dump(
{
'args': args,
'pro_policy': pro_policy,
'adv_policy': adv_policy,
'zero_test': [const_testing_rews],
'rand_test': [rand_testing_rews],
'step_test': [step_testing_rews],
'rand_step_test': [rand_step_testing_rews],
'iter_save': ni,
'exp_save': ne,
'adv_test': [adv_testing_rews]
}, open(save_name + '_' + str(ni) + '.p', 'wb'))
## Shutting down the optimizer ##
pro_algo.shutdown_worker()
## Updating the test summaries over all training instances
const_test_rew_summary.append(const_testing_rews)
rand_test_rew_summary.append(rand_testing_rews)
step_test_rew_summary.append(step_testing_rews)
rand_step_test_rew_summary.append(rand_step_testing_rews)
adv_test_rew_summary.append(adv_testing_rews)
queue.put([
const_test_rew_summary, rand_test_rew_summary, step_test_rew_summary,
rand_step_test_rew_summary, adv_test_rew_summary
])
############ SAVING MODEL ############
'''
def run_task(*_):
v_enter = 30
inner_length = 800
long_length = 100
short_length = 800
n = 1
m = 5
num_cars_left = 3
num_cars_right = 3
num_cars_top = 15
num_cars_bot = 15
tot_cars = (num_cars_left + num_cars_right) * m \
+ (num_cars_bot + num_cars_top) * n
grid_array = {
"short_length": short_length,
"inner_length": inner_length,
"long_length": long_length,
"row_num": n,
"col_num": m,
"cars_left": num_cars_left,
"cars_right": num_cars_right,
"cars_top": num_cars_top,
"cars_bot": num_cars_bot
}
sumo_params = SumoParams(sim_step=1, sumo_binary="sumo-gui")
vehicles = Vehicles()
vehicles.add(veh_id="idm",
acceleration_controller=(SumoCarFollowingController, {}),
sumo_car_following_params=SumoCarFollowingParams(
minGap=2.5,
max_speed=v_enter,
),
routing_controller=(GridRouter, {}),
num_vehicles=tot_cars,
speed_mode="all_checks")
additional_env_params = {
"target_velocity": 50,
"num_steps": 500,
"control-length": 150,
"switch_time": 3.0
}
env_params = EnvParams(additional_params=additional_env_params)
additional_net_params = {
"speed_limit": 35,
"grid_array": grid_array,
"horizontal_lanes": 1,
"vertical_lanes": 1,
"traffic_lights": True
}
initial_config, net_params = get_non_flow_params(10, additional_net_params)
scenario = SimpleGridScenario(name="grid-intersection",
generator_class=SimpleGridGenerator,
vehicles=vehicles,
net_params=net_params,
initial_config=initial_config)
env_name = "GreenWaveEnv"
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 = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=40000,
max_path_length=horizon,
# whole_paths=True,
n_itr=800,
discount=0.999,
# step_size=0.01,
)
algo.train()
from humanoidopt.env import HumanoidOptEnv
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
env = normalize(HumanoidOptEnv())
policy = GaussianMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32)
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=4000,
max_path_length=100,
n_itr=40,
discount=0.99,
step_size=0.01,
)
algo.train()
copyparams.update(modeparams)
copyparams['layer'] = layer
mdp = normalize(GymEnv(params['env'], **copyparams))
for seed in seeds:
policy = GaussianMLPPolicy(env_spec=mdp.spec,
hidden_sizes=(32, 32),
init_std=10)
baseline = LinearFeatureBaseline(mdp.spec, )
batch_size = 50 * 250
algo = TRPO(env=mdp,
policy=policy,
baseline=baseline,
batch_size=batch_size,
whole_paths=True,
max_path_length=50,
n_itr=200,
step_size=0.01,
subsample_factor=1.0,
**copyparams)
run_experiment_lite(
algo.train(),
exp_prefix="r-inception-same-strike-std2",
n_parallel=4,
# dry=True,
snapshot_mode="all",
seed=seed,
mode="ec2_mujoco",
#terminate_machine=False
)
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()
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
sampling_res = 2 if 'sampling_res' not in v.keys() else v['sampling_res']
samples_per_cell = 10 # for the oracle rejection sampling
# Log performance of randomly initialized policy with FIXED goal [0.1, 0.1]
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir() # problem with logger module here!!
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=3)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
inner_env = normalize(PointMazeEnv(maze_id=v['maze_id']))
uniform_goal_generator = UniformStateGenerator(state_size=v['goal_size'],
bounds=v['goal_range'],
center=v['goal_center'])
env = GoalExplorationEnv(
env=inner_env,
goal_generator=uniform_goal_generator,
obs2goal_transform=lambda x: x[:int(len(x) / 2)],
terminal_eps=v['terminal_eps'],
distance_metric=v['distance_metric'],
extend_dist_rew=v['extend_dist_rew'],
only_feasible=v['only_feasible'],
terminate_env=True,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
# Fix the variance since different goals will require different variances, making this parameter hard to learn.
learn_std=v['learn_std'],
adaptive_std=v['adaptive_std'],
std_hidden_sizes=(16,
16), # this is only used if adaptive_std is true!
output_gain=v['output_gain'],
init_std=v['policy_init_std'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
# initialize all logging arrays on itr0
outer_iter = 0
logger.log('Generating the Initial Heatmap...')
test_and_plot_policy(policy,
env,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
center=v['goal_center'],
limit=v['goal_range'])
report.new_row()
all_goals = StateCollection(distance_threshold=v['coll_eps'])
total_rollouts = 0
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
logger.log("Sampling goals")
goals = np.array([]).reshape((-1, v['goal_size']))
k = 0
while goals.shape[0] < v['num_new_goals']:
print('good goals collected: ', goals.shape[0])
logger.log("Sampling and labeling the goals: %d" % k)
k += 1
unif_goals = sample_unif_feas(env,
samples_per_cell=samples_per_cell)
labels = label_states(unif_goals,
env,
policy,
v['horizon'],
n_traj=v['n_traj'],
key='goal_reached')
logger.log("Converting the labels")
init_classes, text_labels = convert_label(labels)
goals = np.concatenate([goals,
unif_goals[init_classes == 2]]).reshape(
(-1, v['goal_size']))
if v['replay_buffer'] and outer_iter > 0 and all_goals.size > 0:
old_goals = all_goals.sample(
v['num_old_goals']) #todo: replay noise?
goals = np.vstack([goals, old_goals])
with ExperimentLogger(log_dir,
'last',
snapshot_mode='last',
hold_outter_log=True):
logger.log("Updating the environment goal generator")
env.update_goal_generator(
UniformListStateGenerator(
goals.tolist(),
persistence=v['persistence'],
with_replacement=v['with_replacement'],
))
logger.log("Training the algorithm")
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=v['pg_batch_size'],
max_path_length=v['horizon'],
n_itr=v['inner_iters'],
step_size=0.01,
plot=False,
)
algo.train()
logger.log('Generating the Heatmap...')
test_and_plot_policy(policy,
env,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
center=v['goal_center'],
limit=v['goal_range'])
logger.log("Labeling the goals")
labels = label_states(goals,
env,
policy,
v['horizon'],
n_traj=v['n_traj'],
key='goal_reached')
plot_labeled_states(goals,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'],
maze_id=v['maze_id'])
# ###### extra for deterministic:
# logger.log("Labeling the goals deterministic")
# with policy.set_std_to_0():
# labels_det = label_states(goals, env, policy, v['horizon'], n_traj=v['n_traj'], n_processes=1)
# plot_labeled_states(goals, labels_det, report=report, itr=outer_iter, limit=v['goal_range'], center=v['goal_center'])
labels = np.logical_and(labels[:, 0],
labels[:, 1]).astype(int).reshape((-1, 1))
# rollouts used for labeling (before TRPO itrs):
num_empty_spaces = len(unwrap_maze(env).find_empty_space())
logger.record_tabular(
'LabelingRollouts',
k * v['n_traj'] * samples_per_cell * num_empty_spaces)
total_rollouts += k * v['n_traj'] * samples_per_cell * num_empty_spaces
logger.record_tabular('TotalLabelingRollouts', total_rollouts)
logger.dump_tabular(with_prefix=False)
report.new_row()
# append new goals to list of all goals (replay buffer): Not the low reward ones!!
filtered_raw_goals = [
goal for goal, label in zip(goals, labels) if label[0] == 1
]
all_goals.append(filtered_raw_goals)
def run_task(*_):
"""Implement the run_task method needed to run experiments with rllab."""
sim_params = AimsunParams(sim_step=0.5, render=False, seed=0)
vehicles = VehicleParams()
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": None,
"max_accel": 1,
"max_decel": 1
}
env_params = EnvParams(horizon=HORIZON,
additional_params=additional_env_params,
warmup_steps=1500)
additional_net_params = {
"length": 230,
"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,
vehicles,
net_params,
initial_config=initial_config)
env_name = "WaveAttenuationPOEnv"
simulator = 'aimsun'
pass_params = (env_name, sim_params, vehicles, env_params, net_params,
initial_config, scenario, simulator)
env = GymEnv(env_name, record_video=False, register_params=pass_params)
horizon = env.horizon
env = normalize(env)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(3, 3),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=15000,
max_path_length=horizon,
n_itr=500,
# whole_paths=True,
discount=0.999,
# step_size=v["step_size"],
)
algo.train(),
env = normalize(CartpoleEnv())
policy = GaussianMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(100, 50, 25)
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=50000,
max_path_length=500,
n_itr=500,
discount=0.99,
step_size=0.1,
)
rets_per_episode_batchwise = algo.train()
rets_per_episode = [x for lst in rets_per_episode_batchwise for x in lst]
print('mean return over all episodes', np.mean(rets_per_episode))
plt.plot(rets_per_episode, alpha=0.3)
plt.savefig('/tmp/upsi/test_rllab/trpo_cartpole.png')
