def init(config,
agent='robot',
her=False,
object_Qfunc=None,
backward_dyn=None,
object_policy=None,
reward_fun=None):
#hyperparameters
ENV_NAME = config['env_id']
SEED = config['random_seed']
if 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME:
env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'])
n_rob_actions = 4
n_actions = config['max_nb_objects'] * len(
config['obj_action_type']) + n_rob_actions
elif 'HandManipulate' in ENV_NAME and 'Multi' in ENV_NAME:
env = gym.make(ENV_NAME, obj_action_type=config['obj_action_type'])
n_rob_actions = 20
n_actions = 1 * len(config['obj_action_type']) + n_rob_actions
else:
env = gym.make(ENV_NAME)
def her_reward_fun(ag_2, g, info): # vectorized
return env.compute_reward(achieved_goal=ag_2,
desired_goal=g,
info=info)
env.seed(SEED)
K.manual_seed(SEED)
np.random.seed(SEED)
#if config['obj_action_type'] == 'all':
# n_actions = config['max_nb_objects'] * 7 + 4
#elif config['obj_action_type'] == 'slide_only':
# n_actions = config['max_nb_objects'] * 3 + 4
#elif config['obj_action_type'] == 'rotation_only':
# n_actions = config['max_nb_objects'] * 4 + 4
observation_space = env.observation_space.spaces['observation'].shape[
1] + env.observation_space.spaces['desired_goal'].shape[0]
action_space = (gym.spaces.Box(-1.,
1.,
shape=(n_rob_actions, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions - n_rob_actions, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32'))
GAMMA = config['gamma']
TAU = config['tau']
ACTOR_LR = config['plcy_lr']
CRITIC_LR = config['crtc_lr']
MEM_SIZE = config['buffer_length']
REGULARIZATION = config['regularization']
NORMALIZED_REWARDS = config['reward_normalization']
OUT_FUNC = K.tanh
if config['agent_alg'] == 'DDPG_BD':
MODEL = DDPG_BD
from olbr.replay_buffer import ReplayBuffer
from olbr.her_sampler import make_sample_her_transitions
elif config['agent_alg'] == 'MADDPG_BD':
MODEL = MADDPG_BD
from olbr.replay_buffer import ReplayBuffer_v2 as ReplayBuffer
from olbr.her_sampler import make_sample_her_transitions_v2 as make_sample_her_transitions
#exploration initialization
if agent == 'robot':
agent_id = 0
noise = (Noise(action_space[0].shape[0], sigma=0.2, eps=0.3),
Noise(action_space[1].shape[0], sigma=0.2, eps=0.3))
env._max_episode_steps *= config['max_nb_objects']
elif agent == 'object':
agent_id = 1
#noise = Noise(action_space[1].shape[0], sigma=0.05, eps=0.1)
noise = Noise(action_space[1].shape[0], sigma=0.2, eps=0.3)
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)
) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
model = MODEL(observation_space,
action_space,
optimizer,
Actor,
Critic,
loss_func,
GAMMA,
TAU,
out_func=OUT_FUNC,
discrete=False,
regularization=REGULARIZATION,
normalized_rewards=NORMALIZED_REWARDS,
agent_id=agent_id,
object_Qfunc=object_Qfunc,
backward_dyn=backward_dyn,
object_policy=object_policy,
reward_fun=reward_fun,
masked_with_r=config['masked_with_r'])
normalizer = [Normalizer(), Normalizer()]
#memory initilization
if her:
sample_her_transitions = make_sample_her_transitions(
'future', 4, her_reward_fun)
else:
sample_her_transitions = make_sample_her_transitions(
'none', 4, her_reward_fun)
buffer_shapes = {
'o': (env._max_episode_steps,
env.observation_space.spaces['observation'].shape[1] * 2),
'ag': (env._max_episode_steps,
env.observation_space.spaces['achieved_goal'].shape[0]),
'g': (env._max_episode_steps,
env.observation_space.spaces['desired_goal'].shape[0]),
'u': (env._max_episode_steps - 1, action_space[2].shape[0])
}
memory = (ReplayBuffer(buffer_shapes, MEM_SIZE, env._max_episode_steps,
sample_her_transitions),
ReplayBuffer(buffer_shapes, MEM_SIZE, env._max_episode_steps,
sample_her_transitions))
experiment_args = (env, memory, noise, config, normalizer, agent_id)
print('singleseeding')
return model, experiment_args
def init(config, agent='robot', her=False,
reward_fun=None,
obj_traj=None,
obj_mean=None,
obj_std=None):
#hyperparameters
ENV_NAME = config['env_id']
SEED = config['random_seed']
N_ENVS = config['n_envs']
def make_env(env_id, i_env, env_type='Fetch', stack_prob=None):
def _f():
if env_type == 'Fetch':
env = gym.make(env_id, n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range']
)
elif env_type == 'FetchStack':
env = gym.make(env_id, n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'],
change_stack_order=config['change_stack_order']
)
elif env_type == 'Hand':
env = gym.make(env_id, obj_action_type=config['obj_action_type'])
elif env_type == 'Others':
env = gym.make(env_id)
#env._max_episode_steps *= config['max_nb_objects']
keys = env.observation_space.spaces.keys()
env = gym.wrappers.FlattenDictWrapper(env, dict_keys=list(keys))
env.seed(SEED+10*i_env)
if stack_prob is not None:
env.unwrapped.stack_prob = stack_prob
return env
return _f
if 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME and 'Stack' in ENV_NAME:
dummy_env = gym.make(ENV_NAME, n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv([make_env(ENV_NAME, i_env, 'FetchStack', config['train_stack_prob']) for i_env in range(N_ENVS)])
envs_test = SubprocVecEnv([make_env(ENV_NAME, i_env, 'FetchStack', config['test_stack_prob']) for i_env in range(N_ENVS)])
envs_render = SubprocVecEnv([make_env(ENV_NAME, i_env, 'FetchStack', config['test_stack_prob']) for i_env in range(1)])
n_rob_actions = 4
n_actions = config['max_nb_objects'] * len(config['obj_action_type']) + n_rob_actions
elif 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME:
dummy_env = gym.make(ENV_NAME, n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv([make_env(ENV_NAME, i_env, 'Fetch') for i_env in range(N_ENVS)])
envs_test = None
envs_render = SubprocVecEnv([make_env(ENV_NAME, i_env, 'Fetch') for i_env in range(1)])
n_rob_actions = 4
n_actions = config['max_nb_objects'] * len(config['obj_action_type']) + n_rob_actions
elif 'HandManipulate' in ENV_NAME and 'Multi' in ENV_NAME:
dummy_env = gym.make(ENV_NAME, obj_action_type=config['obj_action_type'])
envs = SubprocVecEnv([make_env(ENV_NAME, i_env, 'Hand') for i_env in range(N_ENVS)])
envs_test = None
envs_render = SubprocVecEnv([make_env(ENV_NAME, i_env, 'Hand') for i_env in range(1)])
n_rob_actions = 20
n_actions = 1 * len(config['obj_action_type']) + n_rob_actions
elif 'Fetch' in ENV_NAME and 'MaRob' in ENV_NAME:
dummy_env = gym.make(ENV_NAME, n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv([make_env(ENV_NAME, i_env, 'Fetch') for i_env in range(N_ENVS)])
envs_test = None
envs_render = SubprocVecEnv([make_env(ENV_NAME, i_env, 'Fetch') for i_env in range(1)])
n_rob_actions = 4 * 2
n_actions = config['max_nb_objects'] * len(config['obj_action_type']) + n_rob_actions
else:
dummy_env = gym.make(ENV_NAME)
envs = SubprocVecEnv([make_env(ENV_NAME, i_env, 'Others') for i_env in range(N_ENVS)])
envs_test = None
envs_render = None
def make_her_reward_fun(nb_critics, use_step_reward_fun=False):
def _her_reward_fun(ag_2, g, info): # vectorized
goal_len = ag_2.shape[1]//nb_critics
rew = dummy_env.compute_reward(achieved_goal=ag_2[:,0:goal_len], desired_goal=g[:,0:goal_len], info=info)
all_rew = rew.copy()[:, np.newaxis]
for i_reward in range(1,nb_critics):
if not use_step_reward_fun:
obj_rew = dummy_env.compute_reward(achieved_goal=ag_2[:,goal_len*i_reward:goal_len*(i_reward+1)],
desired_goal=g[:,goal_len*i_reward:goal_len*(i_reward+1)], info=info)
else:
goal_a = ag_2[:,goal_len*i_reward:goal_len*(i_reward+1)].reshape(-1,dummy_env.env.n_objects,3)
goal_b = g[:,goal_len*i_reward:goal_len*(i_reward+1)].reshape(-1,dummy_env.env.n_objects,3)
d = np.linalg.norm(goal_a - goal_b, axis=-1)
#obj_rew = - (d > dummy_env.env.distance_threshold).astype(np.float32).sum(-1)
obj_rew = - (d > dummy_env.env.distance_threshold).astype(np.float32)
all_rew = np.concatenate((all_rew, obj_rew.copy()[:, np.newaxis]), axis=-1)
#all_rew = np.concatenate((all_rew, obj_rew.copy()), axis=-1)
return all_rew
return _her_reward_fun
her_reward_fun = make_her_reward_fun(2, config['use_step_reward_fun'])
K.manual_seed(SEED)
np.random.seed(SEED)
observation_space = (dummy_env.observation_space.spaces['observation'].shape[1]*2 + dummy_env.observation_space.spaces['desired_goal'].shape[0]*2,
dummy_env.observation_space.spaces['observation'].shape[1]*1 + dummy_env.observation_space.spaces['desired_goal'].shape[0]*2)
action_space = (gym.spaces.Box(-1., 1., shape=(n_rob_actions,), dtype='float32'),
gym.spaces.Box(-1., 1., shape=(n_actions-n_rob_actions,), dtype='float32'),
gym.spaces.Box(-1., 1., shape=(n_actions,), dtype='float32'))
GAMMA = config['gamma']
clip_Q_neg = config['clip_Q_neg'] if config['clip_Q_neg'] < 0 else None
TAU = config['tau']
ACTOR_LR = config['plcy_lr']
CRITIC_LR = config['crtc_lr']
MEM_SIZE = config['buffer_length']
REGULARIZATION = config['regularization']
NORMALIZED_REWARDS = config['reward_normalization']
OUT_FUNC = K.tanh
MODEL = DDPG_BD
#exploration initialization
noise = Noise(action_space[0].shape[0], sigma=0.2, eps=0.3)
config['episode_length'] = dummy_env._max_episode_steps
config['observation_space'] = dummy_env.observation_space
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
model = MODEL(observation_space, action_space, optimizer,
Actor, Critic, loss_func, GAMMA, TAU, out_func=OUT_FUNC, discrete=False,
regularization=REGULARIZATION, normalized_rewards=NORMALIZED_REWARDS,
reward_fun=reward_fun, clip_Q_neg=clip_Q_neg, nb_critics=config['max_nb_objects']+1
)
model.n_objects = config['max_nb_objects']
class NormalizerObj(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def process(self, achieved, desired):
achieved_out = achieved - K.tensor(self.mean[0], dtype=achieved.dtype, device=achieved.device)
achieved_out /= K.tensor(self.std[0], dtype=achieved.dtype, device=achieved.device)
desired_out = desired - K.tensor(self.mean[1], dtype=desired.dtype, device=desired.device)
desired_out /= K.tensor(self.std[1], dtype=desired.dtype, device=desired.device)
return achieved_out, desired_out
normalizer = [Normalizer(), Normalizer(), NormalizerObj(obj_mean, obj_std)]
model.obj_traj = obj_traj.to('cpu')
model.obj_traj.eval()
for _ in range(1):
state_all = dummy_env.reset()
for i_step in range(config['episode_length']):
model.to_cpu()
obs = [K.tensor(obs, dtype=K.float32).unsqueeze(0) for obs in state_all['observation']]
goal = K.tensor(state_all['desired_goal'], dtype=K.float32).unsqueeze(0)
if i_step%config['objtraj_goal_horizon'] == 0:
achieved_goal = K.tensor(state_all['achieved_goal'], dtype=K.float32).unsqueeze(0)
objtraj_goal = []
goal_len_per_obj = goal.shape[1]//model.n_objects
for i_object in range(model.n_objects):
achieved_goal_per_obj = achieved_goal[:,i_object*goal_len_per_obj:(i_object+1)*goal_len_per_obj]
goal_per_obj = goal[:,i_object*goal_len_per_obj:(i_object+1)*goal_len_per_obj]
normed_achieved_goal_per_obj, normed_goal_per_goal = normalizer[2].process(achieved_goal_per_obj, goal_per_obj)
with K.no_grad():
objtraj_goal_per_obj = model.obj_traj(normed_achieved_goal_per_obj, normed_goal_per_goal)
objtraj_goal.append(objtraj_goal_per_obj)
objtraj_goal = K.cat(objtraj_goal, dim=-1)
# Observation normalization
obs_goal = []
obs_goal.append(K.cat([obs[0], obs[1], goal, objtraj_goal], dim=-1))
if normalizer[0] is not None:
obs_goal[0] = normalizer[0].preprocess_with_update(obs_goal[0])
if config['agent_alg'] == 'DDPG_BD':
action = model.select_action(obs_goal[0], noise).cpu().numpy().squeeze(0)
elif config['agent_alg'] == 'MADDPG_BD':
action = model.select_action(obs_goal[0], noise, goal_size=goal.shape[1]).cpu().numpy().squeeze(0)
action_to_env = np.zeros_like(dummy_env.action_space.sample())
action_to_env[0:action.shape[0]] = action
next_state_all, _, _, _ = dummy_env.step(action_to_env)
# Move to the next state
state_all = next_state_all
#memory initilization
if her:
sample_her_transitions = make_sample_her_transitions('future', 4, her_reward_fun)
else:
sample_her_transitions = make_sample_her_transitions('none', 4, her_reward_fun)
buffer_shapes = {
'o' : (config['episode_length'], dummy_env.observation_space.spaces['observation'].shape[1]*3),
'ag' : (config['episode_length'], dummy_env.observation_space.spaces['achieved_goal'].shape[0]*2),
'g' : (config['episode_length'], dummy_env.observation_space.spaces['desired_goal'].shape[0]*2),
'u' : (config['episode_length']-1, action_space[2].shape[0])
}
memory = ReplayBuffer(buffer_shapes, MEM_SIZE, config['episode_length'], sample_her_transitions)
experiment_args = ((envs, envs_test, envs_render), memory, noise, config, normalizer, None)
return model, experiment_args
def init(config):
if config['resume'] != '':
resume_path = config['resume']
saver = Saver(config)
config, start_episode, save_dict = saver.resume_ckpt()
config['resume'] = resume_path
else:
start_episode = 0
#hyperparameters
ENV_NAME = config['env_id'] #'simple_spread'
SEED = config['random_seed'] # 1
GAMMA = config['gamma'] # 0.95
TAU = config['tau'] # 0.01
ACTOR_LR = config['plcy_lr'] # 0.01
CRITIC_LR = config['crtc_lr'] # 0.01
MEM_SIZE = config['buffer_length'] # 1000000
REGULARIZATION = config['regularization'] # True
NORMALIZED_REWARDS = config['reward_normalization'] # True
if (ENV_NAME == 'FetchStackMulti-v1') or (ENV_NAME == 'FetchPushMulti-v1'):
env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'])
else:
env = gym.make(ENV_NAME)
env.seed(SEED)
if config['obj_action_type'] == 'all':
n_actions = config['max_nb_objects'] * 7 + 4
elif config['obj_action_type'] == 'slide_only':
n_actions = config['max_nb_objects'] * 3 + 4
elif config['obj_action_type'] == 'rotation_only':
n_actions = config['max_nb_objects'] * 4 + 4
observation_space = env.observation_space.spaces['observation'].shape[
1] + env.observation_space.spaces['desired_goal'].shape[0]
action_space = (gym.spaces.Box(-1., 1., shape=(4, ), dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions - 4, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32'))
if env.action_space.low[0] == -1 and env.action_space.high[0] == 1:
OUT_FUNC = K.tanh
elif env.action_space.low[0] == 0 and env.action_space.high[0] == 1:
OUT_FUNC = K.sigmoid
else:
OUT_FUNC = K.sigmoid
K.manual_seed(SEED)
np.random.seed(SEED)
if config['agent_alg'] == 'MADDPG':
MODEL = MADDPG
elif config['agent_alg'] == 'DDPG':
MODEL = DDPG
elif config['agent_alg'] == 'MADDPG_R':
MODEL = MADDPG_R
elif config['agent_alg'] == 'MADDPG_RAE':
MODEL = MADDPG_RAE
if config['verbose'] > 1:
# utils
summaries = (Summarizer(config['dir_summary_train'], config['port'],
config['resume']),
Summarizer(config['dir_summary_test'], config['port'],
config['resume']))
saver = Saver(config)
else:
summaries = None
saver = None
#exploration initialization
noise = (Noise(action_space[0].shape[0], sigma=0.2, eps=0.3),
Noise(action_space[1].shape[0], sigma=0.05, eps=0.1))
#noise = OUNoise(action_space.shape[0])
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)
) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
model = MODEL(observation_space,
action_space,
optimizer,
Actor,
Critic,
loss_func,
GAMMA,
TAU,
out_func=OUT_FUNC,
discrete=False,
regularization=REGULARIZATION,
normalized_rewards=NORMALIZED_REWARDS)
if config['resume'] != '':
for i, param in enumerate(save_dict['model_params']):
model.entities[i].load_state_dict(param)
#memory initilization
#memory = ReplayMemory(MEM_SIZE)
def reward_fun(ag_2, g, info): # vectorized
return env.compute_reward(achieved_goal=ag_2,
desired_goal=g,
info=info)
sample_her_transitions = make_sample_her_transitions(
'future', 4, reward_fun)
buffer_shapes = {
'o': (env._max_episode_steps,
env.observation_space.spaces['observation'].shape[1] * 2),
'ag': (env._max_episode_steps,
env.observation_space.spaces['achieved_goal'].shape[0]),
'g': (env._max_episode_steps,
env.observation_space.spaces['desired_goal'].shape[0]),
'u': (env._max_episode_steps - 1, action_space[2].shape[0])
}
memory = ReplayBuffer(buffer_shapes, MEM_SIZE, env._max_episode_steps,
sample_her_transitions)
normalizer = (Normalizer(), Normalizer())
experiment_args = (env, memory, noise, config, summaries, saver,
start_episode, normalizer)
return model, experiment_args
def init(config,
agent='robot',
her=False,
reward_fun=None,
obj_traj=None,
obj_mean=None,
obj_std=None):
#hyperparameters
ENV_NAME = config['env_id']
SEED = config['random_seed']
N_ENVS = config['n_envs']
def make_env(env_id, i_env, env_type='Fetch', stack_prob=None):
def _f():
if env_type == 'Fetch':
env = gym.make(env_id,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
elif env_type == 'FetchStack':
env = gym.make(env_id,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'],
change_stack_order=config['change_stack_order'])
elif env_type == 'Hand':
env = gym.make(env_id,
obj_action_type=config['obj_action_type'])
elif env_type == 'FetchMaRobSeq':
env = gym.make(env_id,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=np.array([0.15, 0.60]),
widerangeobj=True)
elif env_type == 'FetchMaRobSeqTest':
env = gym.make(env_id,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'],
widerangeobj=False)
elif env_type == 'Others':
env = gym.make(env_id)
#env._max_episode_steps *= config['max_nb_objects']
keys = env.observation_space.spaces.keys()
env = gym.wrappers.FlattenDictWrapper(env, dict_keys=list(keys))
env.seed(SEED + 10 * i_env)
if stack_prob is not None:
env.unwrapped.stack_prob = stack_prob
return env
return _f
if 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME and 'Stack' in ENV_NAME:
dummy_env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'FetchStack', config['train_stack_prob'])
for i_env in range(N_ENVS)
])
envs_test = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'FetchStack', config['test_stack_prob'])
for i_env in range(N_ENVS)
])
envs_render = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'FetchStack', config['test_stack_prob'])
for i_env in range(1)
])
n_rob_actions = 4
n_actions = config['max_nb_objects'] * len(
config['obj_action_type']) + n_rob_actions
elif 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME:
dummy_env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv(
[make_env(ENV_NAME, i_env, 'Fetch') for i_env in range(N_ENVS)])
envs_test = None
envs_render = SubprocVecEnv(
[make_env(ENV_NAME, i_env, 'Fetch') for i_env in range(1)])
n_rob_actions = 4
n_actions = config['max_nb_objects'] * len(
config['obj_action_type']) + n_rob_actions
elif 'HandManipulate' in ENV_NAME and 'Multi' in ENV_NAME:
dummy_env = gym.make(ENV_NAME,
obj_action_type=config['obj_action_type'])
envs = SubprocVecEnv(
[make_env(ENV_NAME, i_env, 'Hand') for i_env in range(N_ENVS)])
envs_test = None
envs_render = SubprocVecEnv(
[make_env(ENV_NAME, i_env, 'Hand') for i_env in range(1)])
n_rob_actions = 20
n_actions = 1 * len(config['obj_action_type']) + n_rob_actions
elif 'Fetch' in ENV_NAME and 'MaRobLong' in ENV_NAME:
dummy_env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv(
[make_env(ENV_NAME, i_env, 'Fetch') for i_env in range(N_ENVS)])
envs_test = None
envs_render = SubprocVecEnv(
[make_env(ENV_NAME, i_env, 'Fetch') for i_env in range(1)])
n_rob_actions = 4 * 2
n_actions = config['max_nb_objects'] * len(
config['obj_action_type']) + n_rob_actions
elif 'Fetch' in ENV_NAME and 'MaRobSeq' in ENV_NAME:
dummy_env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'FetchMaRobSeq')
for i_env in range(N_ENVS)
])
envs_test = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'FetchMaRobSeqTest')
for i_env in range(N_ENVS)
])
envs_render = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'FetchMaRobSeqTest')
for i_env in range(1)
])
n_rob_actions = 4 * 2
n_actions = config['max_nb_objects'] * len(
config['obj_action_type']) + n_rob_actions
else:
dummy_env = gym.make(ENV_NAME)
envs = SubprocVecEnv(
[make_env(ENV_NAME, i_env, 'Others') for i_env in range(N_ENVS)])
envs_test = None
envs_render = None
def her_reward_fun(ag_2, g, info): # vectorized
return dummy_env.compute_reward(achieved_goal=ag_2,
desired_goal=g,
info=info).reshape(-1, 1)
K.manual_seed(SEED)
np.random.seed(SEED)
observation_space = (
dummy_env.observation_space.spaces['observation'].shape[1] * 2 +
dummy_env.observation_space.spaces['desired_goal'].shape[0],
dummy_env.observation_space.spaces['observation'].shape[1] +
dummy_env.observation_space.spaces['desired_goal'].shape[0])
action_space = (gym.spaces.Box(-1.,
1.,
shape=(n_rob_actions, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions - n_rob_actions, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32'))
GAMMA = config['gamma']
clip_Q_neg = config['clip_Q_neg'] if config['clip_Q_neg'] < 0 else None
TAU = config['tau']
ACTOR_LR = config['plcy_lr']
CRITIC_LR = config['crtc_lr']
MEM_SIZE = config['buffer_length']
REGULARIZATION = config['regularization']
NORMALIZED_REWARDS = config['reward_normalization']
OUT_FUNC = K.tanh
if config['agent_alg'] == 'DDPG_BD':
from olbr.algorithms.ddpg_q_schedule import DDPG_BD
elif config['agent_alg'] == 'MADDPG_BD':
from olbr.algorithms.maddpg_q_schedule import DDPG_BD
MODEL = DDPG_BD
#exploration initialization
noise = Noise(action_space[0].shape[0], sigma=0.2, eps=0.3)
config['episode_length'] = dummy_env._max_episode_steps
config['observation_space'] = dummy_env.observation_space
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)
) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
model = MODEL(
observation_space,
action_space,
optimizer,
Actor,
Critic,
loss_func,
GAMMA,
TAU,
out_func=OUT_FUNC,
discrete=False,
regularization=REGULARIZATION,
normalized_rewards=NORMALIZED_REWARDS,
reward_fun=reward_fun,
clip_Q_neg=clip_Q_neg,
nb_critics=config['max_nb_objects'] #or fixing to 3
)
model.n_objects = config['max_nb_objects']
class NormalizerObj(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def process(self, achieved, desired, step):
achieved_out = achieved - K.tensor(
self.mean[0], dtype=achieved.dtype, device=achieved.device)
achieved_out /= K.tensor(self.std[0],
dtype=achieved.dtype,
device=achieved.device)
desired_out = desired - K.tensor(
self.mean[1], dtype=desired.dtype, device=desired.device)
desired_out /= K.tensor(self.std[1],
dtype=desired.dtype,
device=desired.device)
step_out = step - K.tensor(
self.mean[2], dtype=desired.dtype, device=desired.device)
step_out /= K.tensor(self.std[2],
dtype=desired.dtype,
device=desired.device)
return achieved_out, desired_out, step_out
normalizer = [Normalizer(), Normalizer(), NormalizerObj(obj_mean, obj_std)]
model.obj_traj = obj_traj.to('cuda')
model.obj_traj.eval()
for _ in range(1):
state_all = dummy_env.reset()
for i_step in range(config['episode_length']):
model.to_cpu()
obs = [
K.tensor(obs, dtype=K.float32).unsqueeze(0)
for obs in state_all['observation']
]
goal = K.tensor(state_all['desired_goal'],
dtype=K.float32).unsqueeze(0)
if i_step == 0:
objtraj_goal = goal
# Observation normalization
obs_goal = []
obs_goal.append(K.cat([obs[0], obs[1], objtraj_goal], dim=-1))
if normalizer[0] is not None:
obs_goal[0] = normalizer[0].preprocess_with_update(obs_goal[0])
if config['agent_alg'] == 'DDPG_BD':
action = model.select_action(obs_goal[0],
noise).cpu().numpy().squeeze(0)
elif config['agent_alg'] == 'MADDPG_BD':
action = model.select_action(
obs_goal[0], noise,
goal_size=goal.shape[1]).cpu().numpy().squeeze(0)
action_to_env = np.zeros_like(dummy_env.action_space.sample())
action_to_env[0:action.shape[0]] = action
next_state_all, _, _, _ = dummy_env.step(action_to_env)
# Move to the next state
state_all = next_state_all
#memory initilization
if her:
sample_her_transitions = make_sample_her_transitions(
'future', 4, her_reward_fun)
else:
sample_her_transitions = make_sample_her_transitions(
'none', 4, her_reward_fun)
buffer_shapes = {
'o': (config['episode_length'],
dummy_env.observation_space.spaces['observation'].shape[1] * 3),
'ag': (config['episode_length'],
dummy_env.observation_space.spaces['achieved_goal'].shape[0]),
'g': (config['episode_length'],
dummy_env.observation_space.spaces['desired_goal'].shape[0]),
'u': (config['episode_length'] - 1, action_space[2].shape[0])
}
memory = ReplayBuffer(buffer_shapes, MEM_SIZE, config['episode_length'],
sample_her_transitions)
experiment_args = ((envs, envs_test, envs_render), memory, noise, config,
normalizer, None)
print("0.20 - 0.25 - boundary_sample iff original_goal update norm")
return model, experiment_args
def init(config,
agent='robot',
her=False,
object_Qfunc=None,
backward_dyn=None,
object_policy=None,
reward_fun=None):
#hyperparameters
ENV_NAME = config['env_id']
SEED = config['random_seed']
N_ENVS = config['n_envs']
def make_env(env_id, i_env, env_type='Fetch', ai_object=False):
def _f():
if env_type == 'Fetch':
env = gym.make(env_id,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
elif env_type == 'Hand':
env = gym.make(env_id,
obj_action_type=config['obj_action_type'])
elif env_type == 'Others':
env = gym.make(env_id)
keys = env.observation_space.spaces.keys()
env = gym.wrappers.FlattenDictWrapper(env, dict_keys=list(keys))
env.seed(SEED + 10 * i_env)
env.unwrapped.ai_object = ai_object
return env
return _f
if 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME and 'Flex' not in ENV_NAME:
dummy_env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'Fetch', agent == 'object')
for i_env in range(N_ENVS)
])
envs_render = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'Fetch', agent == 'object')
for i_env in range(1)
])
n_rob_actions = 4
n_actions = config['max_nb_objects'] * len(
config['obj_action_type']) + n_rob_actions
elif 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME and 'Flex' in ENV_NAME:
dummy_env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range'])
envs = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'Fetch', agent == 'object')
for i_env in range(N_ENVS)
])
envs_render = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'Fetch', agent == 'object')
for i_env in range(1)
])
n_rob_actions = 4
n_actions = 2 * len(config['obj_action_type']) + n_rob_actions
elif 'HandManipulate' in ENV_NAME and 'Multi' in ENV_NAME:
dummy_env = gym.make(ENV_NAME,
obj_action_type=config['obj_action_type'])
envs = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'Hand', agent == 'object')
for i_env in range(N_ENVS)
])
envs_render = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'Hand', agent == 'object')
for i_env in range(1)
])
n_rob_actions = 20
n_actions = 1 * len(config['obj_action_type']) + n_rob_actions
else:
dummy_env = gym.make(ENV_NAME)
envs = SubprocVecEnv([
make_env(ENV_NAME, i_env, 'Others', agent == 'object')
for i_env in range(N_ENVS)
])
envs_render = None
def her_reward_fun(ag_2, g, info): # vectorized
return dummy_env.compute_reward(achieved_goal=ag_2,
desired_goal=g,
info=info)
K.manual_seed(SEED)
np.random.seed(SEED)
observation_space = dummy_env.observation_space.spaces['observation'].shape[
1] + dummy_env.observation_space.spaces['desired_goal'].shape[0]
action_space = (gym.spaces.Box(-1.,
1.,
shape=(n_rob_actions, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions - n_rob_actions, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32'))
GAMMA = config['gamma']
clip_Q_neg = config['clip_Q_neg'] if config['clip_Q_neg'] < 0 else None
TAU = config['tau']
ACTOR_LR = config['plcy_lr']
CRITIC_LR = config['crtc_lr']
MEM_SIZE = config['buffer_length']
REGULARIZATION = config['regularization']
NORMALIZED_REWARDS = config['reward_normalization']
OUT_FUNC = K.tanh
if config['agent_alg'] == 'DDPG_BD':
MODEL = DDPG_BD
from olbr.replay_buffer import ReplayBuffer
from olbr.her_sampler import make_sample_her_transitions
elif config['agent_alg'] == 'MADDPG_BD':
MODEL = MADDPG_BD
from olbr.replay_buffer import ReplayBuffer_v2 as ReplayBuffer
from olbr.her_sampler import make_sample_her_transitions_v2 as make_sample_her_transitions
#exploration initialization
if agent == 'robot':
agent_id = 0
noise = Noise(action_space[0].shape[0], sigma=0.2, eps=0.3)
elif agent == 'object':
agent_id = 1
#noise = Noise(action_space[1].shape[0], sigma=0.2, eps=0.3)
noise = Noise(action_space[1].shape[0], sigma=0.05, eps=0.2)
config['episode_length'] = dummy_env._max_episode_steps
config['observation_space'] = dummy_env.observation_space
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)
) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
model = MODEL(observation_space,
action_space,
optimizer,
Actor,
Critic,
loss_func,
GAMMA,
TAU,
out_func=OUT_FUNC,
discrete=False,
regularization=REGULARIZATION,
normalized_rewards=NORMALIZED_REWARDS,
agent_id=agent_id,
object_Qfunc=object_Qfunc,
backward_dyn=backward_dyn,
object_policy=object_policy,
reward_fun=reward_fun,
clip_Q_neg=clip_Q_neg,
goal_space=dummy_env.reset()['desired_goal'].shape[0])
normalizer = [Normalizer(), Normalizer()]
for _ in range(1):
state_all = dummy_env.reset()
for _ in range(config['episode_length']):
model.to_cpu()
obs = [
K.tensor(obs, dtype=K.float32).unsqueeze(0)
for obs in state_all['observation']
]
goal = K.tensor(state_all['desired_goal'],
dtype=K.float32).unsqueeze(0)
# Observation normalization
obs_goal = []
obs_goal.append(K.cat([obs[agent_id], goal], dim=-1))
if normalizer[agent_id] is not None:
obs_goal[0] = normalizer[agent_id].preprocess_with_update(
obs_goal[0])
action = model.select_action(obs_goal[0],
noise).cpu().numpy().squeeze(0)
action_to_env = np.zeros_like(dummy_env.action_space.sample())
if agent_id == 0:
action_to_env[0:action.shape[0]] = action
else:
action_to_env[-action.shape[0]::] = action
next_state_all, _, _, _ = dummy_env.step(action_to_env)
# Move to the next state
state_all = next_state_all
#memory initilization
if her:
sample_her_transitions = make_sample_her_transitions(
'future', 4, her_reward_fun)
else:
sample_her_transitions = make_sample_her_transitions(
'none', 4, her_reward_fun)
buffer_shapes = {
'o': (config['episode_length'],
dummy_env.observation_space.spaces['observation'].shape[1] * 2),
'ag': (config['episode_length'],
dummy_env.observation_space.spaces['achieved_goal'].shape[0]),
'g': (config['episode_length'],
dummy_env.observation_space.spaces['desired_goal'].shape[0]),
'u': (config['episode_length'] - 1, action_space[2].shape[0])
}
memory = ReplayBuffer(buffer_shapes, MEM_SIZE, config['episode_length'],
sample_her_transitions)
experiment_args = ((envs, envs_render), memory, noise, config, normalizer,
agent_id)
return model, experiment_args
def init(config,
agent='robot',
her=False,
object_Qfunc=None,
backward_dyn=None,
object_policy=None,
reward_fun=None):
#hyperparameters
ENV_NAME = config['env_id']
SEED = config['random_seed']
if (ENV_NAME
== 'FetchStackMulti-v1') or (ENV_NAME == 'FetchPushMulti-v1') or (
ENV_NAME == 'FetchPickAndPlaceMulti-v1'):
env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'])
else:
env = gym.make(ENV_NAME)
def her_reward_fun(ag_2, g, info): # vectorized
return env.compute_reward(achieved_goal=ag_2,
desired_goal=g,
info=info)
env.seed(SEED)
K.manual_seed(SEED)
np.random.seed(SEED)
#if config['obj_action_type'] == 'all':
# n_actions = config['max_nb_objects'] * 7 + 4
#elif config['obj_action_type'] == 'slide_only':
# n_actions = config['max_nb_objects'] * 3 + 4
#elif config['obj_action_type'] == 'rotation_only':
# n_actions = config['max_nb_objects'] * 4 + 4
n_actions = config['max_nb_objects'] * len(config['obj_action_type']) + 4
observation_space = env.observation_space.spaces['observation'].shape[
1] + env.observation_space.spaces['desired_goal'].shape[0]
action_space = (gym.spaces.Box(-1., 1., shape=(4, ), dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions - 4, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32'))
GAMMA = config['gamma']
TAU = config['tau']
ACTOR_LR = config['plcy_lr']
CRITIC_LR = config['crtc_lr']
MEM_SIZE = config['buffer_length']
REGULARIZATION = config['regularization']
NORMALIZED_REWARDS = config['reward_normalization']
if config['agent_alg'] == 'DDPG_BD':
MODEL = DDPG_BD
OUT_FUNC = K.tanh
from olbr.agents.basic import Actor
from olbr.replay_buffer import ReplayBuffer
from olbr.her_sampler import make_sample_her_transitions
elif config['agent_alg'] == 'MADDPG_BD':
MODEL = MADDPG_BD
OUT_FUNC = K.tanh
from olbr.agents.basic import Actor
from olbr.replay_buffer import ReplayBuffer_v2 as ReplayBuffer
from olbr.her_sampler import make_sample_her_transitions_v2 as make_sample_her_transitions
elif config['agent_alg'] == 'PPO_BD':
MODEL = PPO_BD
OUT_FUNC = 'linear'
from olbr.agents.basic import ActorStoch as Actor
from olbr.replay_buffer import RolloutStorage as ReplayBuffer
#exploration initialization
if agent == 'robot':
agent_id = 0
if config['agent_alg'] == 'PPO_BD':
noise = True
else:
noise = Noise(action_space[0].shape[0], sigma=0.2, eps=0.3)
elif agent == 'object':
agent_id = 1
#noise = Noise(action_space[1].shape[0], sigma=0.05, eps=0.1)
noise = Noise(action_space[1].shape[0], sigma=0.2, eps=0.3)
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)
) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
if config['agent_alg'] == 'PPO_BD':
model = MODEL(observation_space,
action_space,
optimizer,
Actor,
Critic,
config['clip_param'],
config['ppo_epoch'],
config['n_batches'],
config['value_loss_coef'],
config['entropy_coef'],
eps=config['eps'],
max_grad_norm=config['max_grad_norm'],
use_clipped_value_loss=True,
out_func=OUT_FUNC,
discrete=False,
agent_id=agent_id,
object_Qfunc=object_Qfunc,
backward_dyn=backward_dyn,
object_policy=object_policy,
reward_fun=reward_fun,
masked_with_r=config['masked_with_r'])
else:
model = MODEL(observation_space,
action_space,
optimizer,
Actor,
Critic,
loss_func,
GAMMA,
TAU,
out_func=OUT_FUNC,
discrete=False,
regularization=REGULARIZATION,
normalized_rewards=NORMALIZED_REWARDS,
agent_id=agent_id,
object_Qfunc=object_Qfunc,
backward_dyn=backward_dyn,
object_policy=object_policy,
reward_fun=reward_fun,
masked_with_r=config['masked_with_r'])
normalizer = [Normalizer(), Normalizer()]
#memory initilization
if config['agent_alg'] == 'PPO_BD':
memory = ReplayBuffer(env._max_episode_steps - 1, config['n_rollouts'],
(observation_space, ), action_space[0])
else:
if her:
sample_her_transitions = make_sample_her_transitions(
'future', 4, her_reward_fun)
else:
sample_her_transitions = make_sample_her_transitions(
'none', 4, her_reward_fun)
buffer_shapes = {
'o': (env._max_episode_steps,
env.observation_space.spaces['observation'].shape[1] * 2),
'ag': (env._max_episode_steps,
env.observation_space.spaces['achieved_goal'].shape[0]),
'g': (env._max_episode_steps,
env.observation_space.spaces['desired_goal'].shape[0]),
'u': (env._max_episode_steps - 1, action_space[2].shape[0]),
'r': (env._max_episode_steps - 1, 1)
}
memory = ReplayBuffer(buffer_shapes, MEM_SIZE, env._max_episode_steps,
sample_her_transitions)
experiment_args = (env, memory, noise, config, normalizer, agent_id)
print('clipped between -1 and 0, and masked with abs(r), and + r')
return model, experiment_args
def init(config,
agent='robot',
her=False,
object_Qfunc=None,
backward_dyn=None,
object_policy=None):
#hyperparameters
ENV_NAME = config['env_id']
SEED = config['random_seed']
if (ENV_NAME == 'FetchStackMulti-v1') or (ENV_NAME == 'FetchPushMulti-v1'):
env = gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'])
else:
env = gym.make(ENV_NAME)
def reward_fun(ag_2, g, info): # vectorized
return env.compute_reward(achieved_goal=ag_2,
desired_goal=g,
info=info)
env.seed(SEED)
K.manual_seed(SEED)
np.random.seed(SEED)
if config['obj_action_type'] == 'all':
n_actions = config['max_nb_objects'] * 7 + 4
elif config['obj_action_type'] == 'slide_only':
n_actions = config['max_nb_objects'] * 3 + 4
elif config['obj_action_type'] == 'rotation_only':
n_actions = config['max_nb_objects'] * 4 + 4
observation_space = env.observation_space.spaces['observation'].shape[
1] + env.observation_space.spaces['desired_goal'].shape[0]
action_space = (gym.spaces.Box(-1., 1., shape=(4, ), dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions - 4, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32'))
GAMMA = config['gamma']
TAU = config['tau']
ACTOR_LR = config['plcy_lr']
CRITIC_LR = config['crtc_lr']
MEM_SIZE = config['buffer_length']
REGULARIZATION = config['regularization']
NORMALIZED_REWARDS = config['reward_normalization']
OUT_FUNC = K.tanh
MODEL = DDPG_BD
#exploration initialization
if agent == 'robot':
agent_id = 0
noise = Noise(action_space[0].shape[0], sigma=0.2, eps=0.3)
elif agent == 'object':
agent_id = 1
noise = Noise(action_space[1].shape[0], sigma=0.05, eps=0.1)
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)
) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
model = MODEL(observation_space,
action_space,
optimizer,
Actor,
Critic,
loss_func,
GAMMA,
TAU,
out_func=OUT_FUNC,
discrete=False,
regularization=REGULARIZATION,
normalized_rewards=NORMALIZED_REWARDS,
agent_id=agent_id,
object_Qfunc=object_Qfunc,
backward_dyn=backward_dyn,
object_policy=object_policy)
normalizer = [Normalizer(), Normalizer()]
#memory initilization
if her:
sample_her_transitions = make_sample_her_transitions(
'future', 4, reward_fun)
else:
sample_her_transitions = make_sample_her_transitions(
'none', 4, reward_fun)
buffer_shapes = {
'o': (env._max_episode_steps,
env.observation_space.spaces['observation'].shape[1] * 2),
'ag': (env._max_episode_steps,
env.observation_space.spaces['achieved_goal'].shape[0]),
'g': (env._max_episode_steps,
env.observation_space.spaces['desired_goal'].shape[0]),
'u': (env._max_episode_steps - 1, action_space[2].shape[0])
}
memory = ReplayBuffer(buffer_shapes, MEM_SIZE, env._max_episode_steps,
sample_her_transitions)
experiment_args = (env, memory, noise, config, normalizer, agent_id)
return model, experiment_args
def init(config, agent='robot', her=False, object_Qfunc=None,
backward_dyn=None,
object_policy=None,
reward_fun=None,
rnd_models=None):
#hyperparameters
ENV_NAME = config['env_id']
SEED = config['random_seed']
N_ENVS = config['n_envs']
env = []
if 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME:
for i_env in range(N_ENVS):
env.append(gym.make(ENV_NAME, n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range']))
n_rob_actions = 4
n_actions = config['max_nb_objects'] * len(config['obj_action_type']) + n_rob_actions
elif 'HandManipulate' in ENV_NAME and 'Multi' in ENV_NAME:
for i_env in range(N_ENVS):
env.append(gym.make(ENV_NAME, obj_action_type=config['obj_action_type']))
n_rob_actions = 20
n_actions = 1 * len(config['obj_action_type']) + n_rob_actions
else:
for i_env in range(N_ENVS):
env.append(gym.make(ENV_NAME))
for i_env in range(N_ENVS):
env[i_env].seed(SEED+10*i_env)
K.manual_seed(SEED)
np.random.seed(SEED)
observation_space = env[0].observation_space.spaces['observation'].shape[1] + env[0].observation_space.spaces['desired_goal'].shape[0]
action_space = (gym.spaces.Box(-1., 1., shape=(n_rob_actions,), dtype='float32'),
gym.spaces.Box(-1., 1., shape=(n_actions-n_rob_actions,), dtype='float32'),
gym.spaces.Box(-1., 1., shape=(n_actions,), dtype='float32'))
GAMMA = config['gamma']
TAU = config['tau']
ACTOR_LR = config['plcy_lr']
CRITIC_LR = config['crtc_lr']
MEM_SIZE = config['buffer_length']
REGULARIZATION = config['regularization']
NORMALIZED_REWARDS = config['reward_normalization']
MODEL = PPO_BD
OUT_FUNC = 'linear'
#exploration initialization
noise = True
env[0]._max_episode_steps *= config['max_nb_objects']
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
model = MODEL(observation_space, action_space, optimizer, Actor, Critic,
config['clip_param'], config['ppo_epoch'], config['n_batches'], config['value_loss_coef'], config['entropy_coef'],
eps=config['eps'], max_grad_norm=config['max_grad_norm'], use_clipped_value_loss=True,
out_func=OUT_FUNC, discrete=False,
object_Qfunc=object_Qfunc, backward_dyn=backward_dyn,
object_policy=object_policy, reward_fun=reward_fun, masked_with_r=config['masked_with_r'],
rnd_models=rnd_models, pred_th=config['pred_th'])
normalizer = [Normalizer(), Normalizer()]
#memory initilization
memory = ReplayBuffer(env[0]._max_episode_steps-1, config['n_rollouts'], (observation_space,), action_space[0])
running_rintr_mean = RunningMean()
experiment_args = (env, memory, noise, config, normalizer, running_rintr_mean)
return model, experiment_args
def init(config,
agent='robot',
her=False,
object_Qfunc=None,
backward_dyn=None,
object_policy=None,
reward_fun=None):
#hyperparameters
ENV_NAME = config['env_id']
SEED = config['random_seed']
N_ENVS = config['n_envs']
env = []
if 'Fetch' in ENV_NAME and 'Multi' in ENV_NAME:
for i_env in range(N_ENVS):
env.append(
gym.make(ENV_NAME,
n_objects=config['max_nb_objects'],
obj_action_type=config['obj_action_type'],
observe_obj_grp=config['observe_obj_grp'],
obj_range=config['obj_range']))
n_rob_actions = 4
n_actions = config['max_nb_objects'] * len(
config['obj_action_type']) + n_rob_actions
elif 'HandManipulate' in ENV_NAME and 'Multi' in ENV_NAME:
for i_env in range(N_ENVS):
env.append(
gym.make(ENV_NAME, obj_action_type=config['obj_action_type']))
n_rob_actions = 20
n_actions = 1 * len(config['obj_action_type']) + n_rob_actions
else:
for i_env in range(N_ENVS):
env.append(gym.make(ENV_NAME))
def her_reward_fun(ag_2, g, info): # vectorized
return env[0].compute_reward(achieved_goal=ag_2,
desired_goal=g,
info=info)
for i_env in range(N_ENVS):
env[i_env].seed(SEED + 10 * i_env)
K.manual_seed(SEED)
np.random.seed(SEED)
observation_space = env[0].observation_space.spaces['observation'].shape[
1] + env[0].observation_space.spaces['desired_goal'].shape[0]
action_space = (gym.spaces.Box(-1.,
1.,
shape=(n_rob_actions, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions - n_rob_actions, ),
dtype='float32'),
gym.spaces.Box(-1.,
1.,
shape=(n_actions, ),
dtype='float32'))
GAMMA = config['gamma']
TAU = config['tau']
ACTOR_LR = config['plcy_lr']
CRITIC_LR = config['crtc_lr']
MEM_SIZE = config['buffer_length']
REGULARIZATION = config['regularization']
NORMALIZED_REWARDS = config['reward_normalization']
if config['agent_alg'] == 'PPO_BD':
MODEL = PPO_BD
OUT_FUNC = 'linear'
from olbr.replay_buffer import ReplayBuffer
from olbr.her_sampler import make_sample_her_transitions
from olbr.replay_buffer import RolloutStorage as RolloutStorage
#exploration initialization
env[0]._max_episode_steps *= config['max_nb_objects']
noise = (True, Noise(action_space[1].shape[0], sigma=0.2, eps=0.3))
#model initialization
optimizer = (optim.Adam, (ACTOR_LR, CRITIC_LR)
) # optimiser func, (actor_lr, critic_lr)
loss_func = F.mse_loss
model = MODEL(observation_space,
action_space,
optimizer,
Actor,
Critic,
config['clip_param'],
config['ppo_epoch'],
config['n_batches'],
config['value_loss_coef'],
config['entropy_coef'],
eps=config['eps'],
max_grad_norm=config['max_grad_norm'],
use_clipped_value_loss=True,
out_func=OUT_FUNC,
discrete=False,
agent_id=0,
object_Qfunc=object_Qfunc,
backward_dyn=backward_dyn,
object_policy=object_policy,
reward_fun=reward_fun,
masked_with_r=config['masked_with_r'])
normalizer = [Normalizer(), Normalizer()]
#memory initilization
if her:
sample_her_transitions = make_sample_her_transitions(
'future', 4, her_reward_fun)
else:
sample_her_transitions = make_sample_her_transitions(
'none', 4, her_reward_fun)
buffer_shapes = {
'o': (env[0]._max_episode_steps,
env[0].observation_space.spaces['observation'].shape[1] * 2),
'ag': (env[0]._max_episode_steps,
env[0].observation_space.spaces['achieved_goal'].shape[0]),
'g': (env[0]._max_episode_steps,
env[0].observation_space.spaces['desired_goal'].shape[0]),
'u': (env[0]._max_episode_steps - 1, action_space[2].shape[0])
}
memory = (RolloutStorage(env[0]._max_episode_steps - 1,
config['n_rollouts'], (observation_space, ),
action_space[0]),
ReplayBuffer(buffer_shapes, MEM_SIZE, env[0]._max_episode_steps,
sample_her_transitions))
experiment_args = (env, memory, noise, config, normalizer, 0)
return model, experiment_args