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Merge branch 'main' of https://github.com/sisl/InteractionImitation

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ebuehrle
2022-02-26 14:06:22 +01:00
parent 6876cf9625 c28c6c05b7
commit 1e24612347
16 changed files with 289 additions and 78 deletions
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ogail-ppo-options-setobs2.py Normal file
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@@ -0,0 +1,174 @@
# %%
import os
import gym
from src.safe_options.options import gail_ppo, Buffer
from src.core.value import SetValue
from src.safe_options.policy import SetMaskedDiscretePolicy
from src.core.discriminator import DeepsetDiscriminator
import torch
from intersim.envs import IntersimpleLidarFlatRandom
from intersim.envs.intersimple import speed_reward
import functools
from src.util.wrappers import CollisionPenaltyWrapper, TransformObservation, Setobs
import numpy as np
from src.safe_options.options import SafeOptionsEnv
from torch.utils.tensorboard import SummaryWriter
from ray import tune
DIR = os.path.dirname(os.path.abspath(__file__))
option_list = [[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5]],
[(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20]],
[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5, 10, 20]], # was the best in training with single hidden layer, but very slow
[(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20, 40]],
[(vel, time) for vel in [0, 2, 5, 10] for time in [5, 10, 20]],
[(vel, time) for vel in [0, 3, 10] for time in [5, 20, 40]]
]
obs_min = np.array([
[-1000, -1000, 0, -np.pi, -1e-1, 0.],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
]).reshape(-1)
obs_max = np.array([
[1000, 1000, 20, np.pi, 1e-1, 0.],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
]).reshape(-1)
def training_function(config):
np.random.seed(config['seed'])
torch.manual_seed(config['seed'])
envs = sum([[SafeOptionsEnv(Setobs(
TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
n_rays=5,
reward=functools.partial(
speed_reward,
collision_penalty=0
),
stop_on_collision=config['stop_on_collision'], track=track,
), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
), options=option_list[config['policy']['option']], safe_actions_collision_method=None,
abort_unsafe_collision_method=None) for _ in range(20)] for track in range(4)],[])
env_fn = lambda i: envs[i]
policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n,
n_hidden_layers=config['policy']['n_hidden_layers'],
hidden_layer_size=config['policy']['hidden_layer_size'],
activation=config['policy']['activation'] ) # config net architecture
pi_opt = torch.optim.Adam(policy.parameters(), lr=config['policy']['learning_rate'])
pi_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(pi_opt, gamma=config['policy']['learning_rate_decay'])
value = SetValue() # config net architecture
v_opt = torch.optim.Adam(value.parameters(), lr=config['value']['learning_rate'])
discriminator = DeepsetDiscriminator() # config net architecture
disc_opt = torch.optim.Adam(discriminator.parameters(), lr=config['discriminator']['learning_rate'], weight_decay=config['discriminator']['weight_decay'])
expert_data = [
torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track0.pt')),
torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track1.pt')),
torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track2.pt')),
torch.load(os.path.join(DIR, 'intersimple-expert-data-setobs2-loc0-track3.pt')),
]
d0 = [d[0] for d in expert_data]
d1 = [d[1] for d in expert_data]
d2 = [d[2] for d in expert_data]
d3 = [d[3] for d in expert_data]
expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3))
expert_data = Buffer(*expert_data)
def callback(info):
tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode'],
disc_mean_reward_per_episode=info['disc/mean_reward_per_episode'],
mean_episode_length=info['gen/mean_episode_length'])
# save model checkpoints
ep = info['epoch'] + 1
if (ep % 25 == 0):
torch.save(info['policy'].state_dict(), f'policy_epoch{ep}.pt')
value, policy = gail_ppo(
env_fn=env_fn,
expert_data=expert_data,
discriminator=discriminator,
disc_opt=disc_opt,
disc_iters=config['discriminator']['iterations_per_epoch'],
policy=policy,
value=value,
v_opt=v_opt,
v_iters=config['value']['iterations_per_epoch'],
epochs=200,
rollout_episodes=60,
rollout_steps=60,
gamma=0.99,
gae_lambda=0.9,
clip_ratio=config['policy']['clip_ratio'],
pi_opt=pi_opt,
pi_iters=config['policy']['iterations_per_epoch'],
logger=SummaryWriter(comment='sgail-ppo-options-setobs2'),
callback=callback,
lr_schedulers=[pi_lr_scheduler],
)
# save model
torch.save(policy.state_dict(), 'policy_final.pt')
analysis = tune.run(
training_function,
config={
'stop_on_collision': tune.grid_search([True, False]),
'policy': {
'learning_rate': 3e-4, # tune.grid_search([3e-4]),
'learning_rate_decay': 1.0, #tune.grid_search([1.0]),
'clip_ratio': 0.2, #tune.grid_search([0.2]),
'iterations_per_epoch': 100, #tune.grid_search([100]),
'hidden_layer_size': tune.grid_search([10, 20, 40]),
'n_hidden_layers': tune.grid_search([2, 3, 4]),
'activation':tune.grid_search([torch.nn.LeakyReLU, torch.nn.Tanh]),
'option': tune.grid_search(list(range(len(option_list))))
},
'value': {
'learning_rate': 1e-3, # tune.grid_search([1e-3]),
'iterations_per_epoch': 1000, #tune.grid_search([1000]),
},
'discriminator': {
'learning_rate': 1e-3, #tune.grid_search([1e-3]),
'weight_decay': 1e-4, #tune.grid_search([1e-4]),
'iterations_per_epoch': 100, #tune.grid_search([100]),
},
'seed': 0,
}
)
print('Best config: ', analysis.get_best_config(metric='gen_mean_reward_per_episode', mode='max'))
# %%
# policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n)
# policy(torch.zeros(env_fn(0).observation_space['observation'].shape), torch.zeros(env_fn(0).observation_space['safe_actions'].shape))
# policy.load_state_dict(torch.load('sgail-ppo-options-setobs2.pt'))
# env = env_fn(0)
# obs = env.reset()
# env.render(mode='post')
# for i in range(300):
# action = policy.sample(policy(
# torch.tensor(obs['observation'], dtype=torch.float32),
# torch.tensor(obs['safe_actions'], dtype=torch.float32),
# ))
# obs, reward, done, _ = env.step(action, render_mode='post')
# print('step', i, 'reward', reward, 'safe actions', obs['safe_actions'])
# if done:
# break
# env.close()
# %%

2
requirements.txt
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@@ -8,3 +8,5 @@ ray[tune]
hyperopt
psutil
fire
stable_baselines3
tensorboard

86
sgail-ppo-options-setobs2.py
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@@ -6,7 +6,8 @@ from src.safe_options.options import gail_ppo, Buffer
from src.core.value import SetValue
from src.safe_options.policy import SetMaskedDiscretePolicy
from src.core.discriminator import DeepsetDiscriminator
import torch.optim
import torch
from intersim.envs import IntersimpleLidarFlatRandom
from intersim.envs.intersimple import speed_reward
import functools
@@ -18,27 +19,38 @@ from ray import tune
from datetime import datetime
import json
def training_function(config):
DIR = os.path.dirname(os.path.abspath(__file__))
obs_min = np.array([
DIR = os.path.dirname(os.path.abspath(__file__))
option_list = [[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5]],
[(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20]],
[(vel, time) for vel in [0, 1, 2, 4, 6, 8, 10] for time in [5, 10, 20]], # was the best in training with single hidden layer, but very slow
[(vel, time) for vel in [0, 1, 2, 5, 7.5, 10] for time in [5, 20, 40]],
[(vel, time) for vel in [0, 2, 5, 10] for time in [5, 10, 20]],
[(vel, time) for vel in [0, 3, 10] for time in [5, 20, 40]]
]
obs_min = np.array([
[-1000, -1000, 0, -np.pi, -1e-1, 0.],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
[0, -np.pi, -20, -20, -np.pi, -1e-1],
]).reshape(-1)
]).reshape(-1)
obs_max = np.array([
obs_max = np.array([
[1000, 1000, 20, np.pi, 1e-1, 0.],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
[50, np.pi, 20, 20, np.pi, 1e-1],
]).reshape(-1)
]).reshape(-1)
envs = [SafeOptionsEnv(Setobs(
def training_function(config):
np.random.seed(config['seed'])
torch.manual_seed(config['seed'])
envs = sum([[SafeOptionsEnv(Setobs(
TransformObservation(CollisionPenaltyWrapper(IntersimpleLidarFlatRandom(
n_rays=5,
reward=functools.partial(
@@ -46,15 +58,18 @@ def training_function(config):
collision_penalty=0
),
check_collisions=True,
stop_on_collision=config['env']['stop_on_collision'],
stop_on_collision=config['env']['stop_on_collision'], track=track,
), collision_distance=6, collision_penalty=100), lambda obs: (obs - obs_min) / (obs_max - obs_min + 1e-10))
), options=[(0, 5), (1, 5), (2, 5), (4, 5), (6, 5), (8, 5), (10, 5)],
), options=option_list[config['policy']['option']],
safe_actions_collision_method=config['env']['safe_actions_collision_method'],
abort_unsafe_collision_method=config['env']['abort_unsafe_collision_method']) for _ in range(60)]
abort_unsafe_collision_method=config['env']['abort_unsafe_collision_method']) for _ in range(20)] for track in range(4)],[])
env_fn = lambda i: envs[i]
policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n, hidden_layer_size=config['policy']['hidden_layer_size']) # config net architecture
policy = SetMaskedDiscretePolicy(env_fn(0).action_space.n,
n_hidden_layers=config['policy']['n_hidden_layers'],
hidden_layer_size=config['policy']['hidden_layer_size'],
activation=config['policy']['activation'] ) # config net architecture
pi_opt = torch.optim.Adam(policy.parameters(), lr=config['policy']['learning_rate'])
pi_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(pi_opt, gamma=config['policy']['learning_rate_decay'])
@@ -77,16 +92,20 @@ def training_function(config):
expert_data = (torch.cat(d0), torch.cat(d1), torch.cat(d2), torch.cat(d3))
expert_data = Buffer(*expert_data)
folder = str(datetime.now())
os.mkdir(os.path.join(DIR, folder))
with open(os.path.join(DIR, folder, 'config.json'), 'w') as f:
run_folder = str(datetime.now())
os.mkdir(os.path.join(DIR, run_folder))
with open(os.path.join(DIR, run_folder, 'config.json'), 'w') as f:
json.dump(config, f, indent=4)
def callback(info):
tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode'])
if not info['epoch'] % 10:
torch.save(policy.state_dict(), os.path.join(DIR, folder, f'sgail-ppo-options-setobs2-{info["epoch"]}.pt'))
torch.save(value.state_dict(), os.path.join(DIR, folder, f'sgail-ppo-options-setobs2-value-{info["epoch"]}.pt'))
tune.report(gen_mean_reward_per_episode=info['gen/mean_reward_per_episode'],
disc_mean_reward_per_episode=info['disc/mean_reward_per_episode'],
mean_episode_length=info['gen/mean_episode_length'])
# save model checkpoints
ep = info['epoch'] + 1
if (ep % 25 == 0):
torch.save(info['policy'].state_dict(), os.path.join(DIR, run_folder, f'policy_epoch{ep}.pt'))
value, policy = gail_ppo(
env_fn=env_fn,
@@ -111,6 +130,9 @@ def training_function(config):
lr_schedulers=[pi_lr_scheduler],
)
# save model
torch.save(policy.state_dict(), 'policy_final.pt')
analysis = tune.run(
training_function,
config={
@@ -120,21 +142,25 @@ analysis = tune.run(
'abort_unsafe_collision_method': 'circle',
},
'policy': {
'learning_rate': tune.grid_search([3e-4]),
'learning_rate_decay': tune.grid_search([1.0]),
'clip_ratio': tune.grid_search([0.2]),
'iterations_per_epoch': tune.grid_search([100]),
'hidden_layer_size': tune.grid_search([25])
'learning_rate': 3e-4, # tune.grid_search([3e-4]),
'learning_rate_decay': 1.0, #tune.grid_search([1.0]),
'clip_ratio': 0.2, #tune.grid_search([0.2]),
'iterations_per_epoch': 100, #tune.grid_search([100]),
'hidden_layer_size': tune.grid_search([10, 20, 40]),
'n_hidden_layers': tune.grid_search([2, 3, 4]),
'activation':tune.grid_search([torch.nn.LeakyReLU, torch.nn.Tanh]),
'option': tune.grid_search(list(range(len(option_list))))
},
'value': {
'learning_rate': tune.grid_search([1e-3]),
'iterations_per_epoch': tune.grid_search([1000]),
'learning_rate': 1e-3, # tune.grid_search([1e-3]),
'iterations_per_epoch': 1000, #tune.grid_search([1000]),
},
'discriminator': {
'learning_rate': tune.grid_search([1e-3]),
'weight_decay': tune.grid_search([1e-4]),
'iterations_per_epoch': tune.grid_search([500]),
}
'learning_rate': 1e-3, #tune.grid_search([1e-3]),
'weight_decay': 1e-4, #tune.grid_search([1e-4]),
'iterations_per_epoch': 100, #tune.grid_search([100]),
},
'seed': 0,
}
)

33
src/core/policy.py
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@@ -36,30 +36,31 @@ class BasePolicy(nn.Module):
class Policy(BasePolicy):
def __init__(self, *args, **kwargs):
def __init__(self, *args, hidden_layer_size=50, n_hidden_layers=2, activation=nn.Tanh, **kwargs):
super().__init__(*args, **kwargs)
self.nn = nn.Sequential(
nn.LazyLinear(50),
nn.Tanh(),
nn.LazyLinear(50),
nn.Tanh(),
nn.LazyLinear(2 * self.action_dim),
)
layers = sum([[nn.LazyLinear(hidden_layer_size),
activation()] for _ in range(n_hidden_layers)],[])
self.nn = nn.Sequential(*layers, nn.LazyLinear(2 *self.action_dim))
# old
# self.nn = nn.Sequential(
# nn.LazyLinear(50),
# nn.Tanh(),
# nn.LazyLinear(50),
# nn.Tanh(),
# nn.LazyLinear(2 * self.action_dim),
#)
def forward(self, states):
return self.nn(states)
class DiscretePolicy(BasePolicy):
def __init__(self, *args, hidden_layer_size=50, **kwargs):
def __init__(self, *args, hidden_layer_size=50, n_hidden_layers=2, activation=nn.Tanh, **kwargs):
super().__init__(*args, **kwargs)
self.nn = nn.Sequential(
nn.LazyLinear(hidden_layer_size),
nn.Tanh(),
nn.LazyLinear(hidden_layer_size),
nn.Tanh(),
nn.LazyLinear(self.action_dim),
)
layers = sum([[nn.LazyLinear(hidden_layer_size),
activation()] for _ in range(n_hidden_layers)],[])
self.nn = nn.Sequential(*layers, nn.LazyLinear(self.action_dim))
def forward(self, states):
return self.nn(states)

15
src/eval_main.py
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@@ -37,21 +37,22 @@ def load_policy(method:str,
Returns:
policy (Optional[BaseAlgorithm]): the policy to evaluate
"""
ml = torch.device('cpu') if not torch.cuda.is_available() else None
if method == 'idm':
policy = IDMRulePolicy(env, **policy_kwargs)
elif method == 'bc':
policy = SetPolicy(env.action_space.shape[-1])
policy.load_state_dict(torch.load(policy_file))
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'gail':
policy = SetPolicy(env.action_space.shape[-1])
policy(torch.zeros(env.observation_space.shape))
policy = ReparamPolicy(policy)
policy.load_state_dict(torch.load(policy_file))
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'gail-ppo':
policy = SetPolicy(env.action_space.shape[-1])
policy.load_state_dict(torch.load(policy_file))
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'rail':
raise NotImplementedError
@@ -59,11 +60,11 @@ def load_policy(method:str,
policy = SetDiscretePolicy(env.action_space.n)
policy(torch.zeros(env.observation_space.shape))
policy = ReparamPolicy(policy)
policy.load_state_dict(torch.load(policy_file))
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'ogail-ppo':
policy = SetDiscretePolicy(env.action_space.n)
policy.load_state_dict(torch.load(policy_file))
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'sgail':
policy = SetMaskedDiscretePolicy(env.action_space.n)
@@ -72,11 +73,11 @@ def load_policy(method:str,
torch.zeros(env.observation_space['safe_actions'].shape)
)
policy = ReparamSafePolicy(policy)
policy.load_state_dict(torch.load(policy_file))
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
elif method == 'sgail-ppo':
policy = SetMaskedDiscretePolicy(env.action_space.n)
policy.load_state_dict(torch.load(policy_file))
policy.load_state_dict(torch.load(policy_file, map_location=ml))
policy.eval()
else:
raise NotImplementedError

25
src/safe_options/options.py
View File

@@ -52,7 +52,8 @@ def gail(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, value
generator_data.ll.actions += 0.1 * torch.randn_like(generator_data.ll.actions)
logger.add_scalar('gen/mean_episode_length', (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0], epoch)
gen_mean_episode_length = (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0]
logger.add_scalar('gen/mean_episode_length', gen_mean_episode_length , epoch)
gen_mean_reward_per_episode = generator_data.hl.rewards[~generator_data.hl.dones].sum() / generator_data.hl.states.shape[0]
logger.add_scalar('gen/mean_reward_per_episode', gen_mean_reward_per_episode, epoch)
logger.add_scalar('gen/unsafe_probability_mass', policy.unsafe_probability_mass(policy(generator_data.hl.states[~generator_data.hl.dones], generator_data.hl.safe_actions[~generator_data.hl.dones])).mean(), epoch)
@@ -64,7 +65,8 @@ def gail(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, value
else:
generator_data.ll.rewards = -F.logsigmoid(discriminator(generator_data.ll.states, generator_data.ll.actions))
logger.add_scalar('disc/final_loss', loss, epoch)
logger.add_scalar('disc/mean_reward_per_episode', generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0], epoch)
disc_mean_reward_per_episode = generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0]
logger.add_scalar('disc/mean_reward_per_episode', disc_mean_reward_per_episode , epoch)
#assert generator_data.ll.rewards.shape == generator_data.ll.dones.shape
generator_data.hl.rewards = torch.where(~generator_data.ll.dones, generator_data.ll.rewards, torch.tensor(0.)).sum(-1)
@@ -77,7 +79,9 @@ def gail(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, value
'epoch': epoch,
'value': value,
'policy': policy,
'gen/mean_reward_per_episode': gen_mean_reward_per_episode,
'gen/mean_episode_length': gen_mean_episode_length.item(),
'gen/mean_reward_per_episode': gen_mean_reward_per_episode.item(),
'disc/mean_reward_per_episode': disc_mean_reward_per_episode.item(),
})
return value, policy
@@ -94,8 +98,8 @@ def gail_ppo(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, v
generator_data = OptionsRollout(HLBuffer(*hl_data), Buffer(*ll_data))
generator_data.ll.actions += 0.1 * torch.randn_like(generator_data.ll.actions)
logger.add_scalar('gen/mean_episode_length', (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0], epoch)
gen_mean_episode_length = (~generator_data.ll.dones).sum() / generator_data.ll.states.shape[0]
logger.add_scalar('gen/mean_episode_length', gen_mean_episode_length, epoch)
gen_mean_reward_per_episode = generator_data.hl.rewards[~generator_data.hl.dones].sum() / generator_data.hl.states.shape[0]
logger.add_scalar('gen/mean_reward_per_episode', gen_mean_reward_per_episode, epoch)
logger.add_scalar('gen/unsafe_probability_mass', policy.unsafe_probability_mass(policy(generator_data.hl.states[~generator_data.hl.dones], generator_data.hl.safe_actions[~generator_data.hl.dones])).mean(), epoch)
@@ -107,7 +111,8 @@ def gail_ppo(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, v
else:
generator_data.ll.rewards = -F.logsigmoid(discriminator(generator_data.ll.states, generator_data.ll.actions))
logger.add_scalar('disc/final_loss', loss, epoch)
logger.add_scalar('disc/mean_reward_per_episode', generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0], epoch)
disc_mean_reward_per_episode = generator_data.ll.rewards[~generator_data.ll.dones].sum() / generator_data.ll.states.shape[0]
logger.add_scalar('disc/mean_reward_per_episode', disc_mean_reward_per_episode, epoch)
#assert generator_data.ll.rewards.shape == generator_data.ll.dones.shape
generator_data.hl.rewards = torch.where(~generator_data.ll.dones, generator_data.ll.rewards, torch.tensor(0.)).sum(-1)
@@ -120,7 +125,9 @@ def gail_ppo(env_fn, expert_data, discriminator, disc_opt, disc_iters, policy, v
'epoch': epoch,
'value': value,
'policy': policy,
'gen/mean_reward_per_episode': gen_mean_reward_per_episode,
'gen/mean_episode_length': gen_mean_episode_length.item(),
'gen/mean_reward_per_episode': gen_mean_reward_per_episode.item(),
'disc/mean_reward_per_episode': disc_mean_reward_per_episode.item(),
})
for lr_scheduler in lr_schedulers:
@@ -248,8 +255,8 @@ class SafeOptionsEnv(OptionsEnv):
if d:
break
if self.abort_unsafe_collision_method is not None and \
not feasible(self.env, plan[k:], method=self.abort_unsafe_collision_method):
if self.abort_unsafe_collision_method is not None:
if not feasible(self.env, plan[k:], method=self.abort_unsafe_collision_method):
break
n_steps = k + 1