def experiment(variant):
ptu.set_gpu_mode(variant['gpu'])
env = NormalizedBoxEnv(Pusher2D3DofGoalCompoEnv(**variant['env_params']))
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
n_unintentional = 2
net_size = variant['net_size']
u_qfs = [
NNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=(net_size, net_size))
for _ in range(n_unintentional)
]
# i_qf = AvgNNQFunction(obs_dim=obs_dim,
i_qf = SumNNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
q_functions=u_qfs)
# _i_policy = TanhGaussianPolicy(
u_policies = [
StochasticPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,
) for _ in range(n_unintentional)
]
i_policy = StochasticPolicy(
hidden_sizes=[net_size, net_size],
obs_dim=obs_dim,
action_dim=action_dim,
)
replay_buffer = MultiGoalReplayBuffer(
variant['algo_params']['replay_buffer_size'],
np.prod(env.observation_space.shape), np.prod(env.action_space.shape),
n_unintentional)
variant['algo_params']['replay_buffer'] = replay_buffer
# QF Plot
variant['algo_params']['_epoch_plotter'] = None
algorithm = IUSQL(env=env,
training_env=env,
save_environment=False,
u_qfs=u_qfs,
u_policies=u_policies,
i_policy=i_policy,
i_qf=i_qf,
algo_interface='torch',
min_buffer_size=variant['algo_params']['batch_size'],
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.train(online=True)
return algorithm
def simulate_policy(args):
np.random.seed(SEED)
ptu.seed(SEED)
data = joblib.load(args.file)
if args.deterministic:
if args.un > -1:
print('Using the deterministic version of the UNintentional policy '
'%02d.' % args.un)
if 'u_policy' in data:
policy = MakeDeterministic(
MultiPolicySelector(data['u_policy'], args.un))
# WeightedMultiPolicySelector(data['u_policy'], args.un))
else:
# policy = MakeDeterministic(data['u_policies'][args.un])
if isinstance(data['policy'], TanhGaussianPolicy):
policy = MakeDeterministic(data['policy'])
else:
policy = MakeDeterministic(
WeightedMultiPolicySelector(data['policy'], args.un)
)
else:
print('Using the deterministic version of the Intentional policy.')
if isinstance(data['policy'], ExplorationPolicy):
policy = MakeDeterministic(data['policy'])
else:
policy = data['policy']
else:
if args.un > -1:
print('Using the UNintentional stochastic policy %02d' % args.un)
if 'u_policy' in data:
# policy = MultiPolicySelector(data['u_policy'], args.un)
policy = WeightedMultiPolicySelector(data['u_policy'], args.un)
else:
policy = WeightedMultiPolicySelector(data['policy'], args.un)
# policy = data['policy'][args.un]
else:
print('Using the Intentional stochastic policy.')
# policy = data['exploration_policy']
policy = data['policy']
print("Policy loaded!!")
# Load environment
dirname = os.path.dirname(args.file)
with open(os.path.join(dirname, 'variant.json')) as json_data:
log_data = json.load(json_data)
env_params = log_data['env_params']
H = int(log_data['path_length'])
env_params.pop('goal', None)
env_params['is_render'] = True
if args.subtask and args.un != -1:
env_params['subtask'] = args.un
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**env_params),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
print("Environment loaded!!")
if args.gpu:
set_gpu_mode(True)
policy.cuda()
if isinstance(policy, MakeDeterministic):
if isinstance(policy.stochastic_policy, PyTorchModule):
policy.stochastic_policy.train(False)
else:
if isinstance(policy, PyTorchModule):
policy.train(False)
while True:
if args.record:
rollout_start_fcn = lambda: \
env.start_recording_video('pusher_video.mp4')
rollout_end_fcn = lambda: \
env.stop_recording_video()
else:
rollout_start_fcn = None
rollout_end_fcn = None
obs_normalizer = data.get('obs_normalizer')
if args.H != -1:
H = args.H
path = rollout(
env,
policy,
max_path_length=H,
animated=True,
obs_normalizer=obs_normalizer,
rollout_start_fcn=rollout_start_fcn,
rollout_end_fcn=rollout_end_fcn,
)
# plot_rollout_reward(path)
if hasattr(env, "log_diagnostics"):
env.log_diagnostics([path])
logger.dump_tabular()
if args.record:
break
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
# Set seeds
np.random.seed(variant['seed'])
ptu.set_gpu_mode(variant['gpu'])
ptu.seed(variant['seed'])
variant['env_params']['seed'] = variant['seed']
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = env.obs_dim
action_dim = env.action_dim
n_unintentional = 2
if variant['load_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
i_qf = data['qf']
u_qf = data['u_qf']
policy = data['policy']
exploration_policy = data['exploration_policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
u_qf = NNMultiQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
n_qs=n_unintentional,
hidden_activation=variant['hidden_activation'],
# shared_hidden_sizes=[net_size, net_size],
shared_hidden_sizes=[net_size],
# shared_hidden_sizes=[],
unshared_hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
i_qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=variant['hidden_activation'],
hidden_sizes=[net_size, net_size],
hidden_w_init=variant['q_hidden_w_init'],
output_w_init=variant['q_output_w_init'],
)
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
n_policies=n_unintentional,
hidden_activation=variant['hidden_activation'],
# shared_hidden_sizes=[net_size, net_size],
shared_hidden_sizes=[net_size],
# shared_hidden_sizes=[],
unshared_hidden_sizes=[net_size, net_size],
unshared_mix_hidden_sizes=[net_size, net_size],
stds=None,
input_norm=variant['input_norm'],
shared_layer_norm=variant['shared_layer_norm'],
policies_layer_norm=variant['policies_layer_norm'],
mixture_layer_norm=variant['mixture_layer_norm'],
mixing_temperature=1.,
softmax_weights=variant['softmax_weights'],
hidden_w_init=variant['pol_hidden_w_init'],
output_w_init=variant['pol_output_w_init'],
)
if INIT_AVG_MIXING:
set_average_mixing(
policy,
n_unintentional,
obs_dim,
batch_size=50,
total_iters=1000,
)
es = OUStrategy(
action_space=env.action_space,
mu=0,
theta=0.15,
max_sigma=0.3,
min_sigma=0.3,
decay_period=100000,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
replay_buffer = MultiGoalReplayBuffer(
max_replay_buffer_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
reward_vector_size=n_unintentional,
)
algorithm = HIUDDPG(env=env,
policy=policy,
explo_policy=exploration_policy,
u_qf=u_qf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
i_qf=i_qf,
eval_env=env,
save_environment=False,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
# algorithm.pretrain(PATH_LENGTH*2)
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
np.random.seed(SEED)
ptu.set_gpu_mode(variant['gpu'])
ptu.seed(SEED)
goal = variant['env_params'].get('goal')
variant['env_params']['goal_poses'] = \
[goal, (goal[0], 'any'), ('any', goal[1])]
variant['env_params'].pop('goal')
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
if variant['log_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
qf = data['qf']
policy = data['policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size]
)
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
# Clamp model parameters
qf.clamp_all_params(min=-0.003, max=0.003)
policy.clamp_all_params(min=-0.003, max=0.003)
replay_buffer = SimpleReplayBuffer(
max_replay_buffer_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = PPO(
env=env,
policy=policy,
qf=qf,
# replay_buffer=replay_buffer,
# batch_size=BATCH_SIZE,
eval_env=env,
save_environment=False,
**variant['algo_params']
)
if ptu.gpu_enabled():
algorithm.cuda()
# algorithm.pretrain(PATH_LENGTH*2)
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
# Set seeds
np.random.seed(variant['seed'])
ptu.set_gpu_mode(variant['gpu'], gpu_id=0)
ptu.seed(variant['seed'])
variant['env_params']['seed'] = variant['seed']
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = env.obs_dim
action_dim = env.action_dim
if variant['load_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
start_epoch = data['epoch']
qf = data['qf']
qf2 = data['qf2']
vf = data['vf']
policy = data['policy']
env._obs_mean = data['obs_mean']
env._obs_var = data['obs_var']
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=expt_params['hidden_activation'],
hidden_sizes=[net_size, net_size],
)
if USE_Q2:
qf2 = NNQFunction(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=expt_params['hidden_activation'],
hidden_sizes=[net_size, net_size],
)
else:
qf2 = None
vf = NNVFunction(
obs_dim=obs_dim,
hidden_activation=expt_params['hidden_activation'],
hidden_sizes=[net_size, net_size],
)
policy = POLICY(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_activation=expt_params['hidden_activation'],
hidden_sizes=[net_size, net_size],
)
# # Clamp model parameters
# qf.clamp_all_params(min=-0.003, max=0.003)
# vf.clamp_all_params(min=-0.003, max=0.003)
# policy.clamp_all_params(min=-0.003, max=0.003)
# if USE_Q2:
# qf2.clamp_all_params(min=-0.003, max=0.003)
replay_buffer = SimpleReplayBuffer(
max_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = SAC(explo_env=env,
policy=policy,
qf=qf,
vf=vf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
qf2=qf2,
eval_env=env,
save_environment=False,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
algorithm.pretrain(variant['steps_pretrain'])
algorithm.train(start_epoch=start_epoch)
return algorithm
def experiment(variant):
# os.environ['OMP_NUM_THREADS'] = str(NP_THREADS)
np.random.seed(SEED)
ptu.set_gpu_mode(variant['gpu'])
ptu.seed(SEED)
goal = variant['env_params'].get('goal')
variant['env_params']['goal_poses'] = \
[goal, (goal[0], 'any'), ('any', goal[1])]
variant['env_params'].pop('goal')
env = NormalizedBoxEnv(
Pusher2D3DofGoalCompoEnv(**variant['env_params']),
# normalize_obs=True,
normalize_obs=False,
online_normalization=False,
obs_mean=None,
obs_var=None,
obs_alpha=0.001,
)
obs_dim = int(np.prod(env.observation_space.shape))
action_dim = int(np.prod(env.action_space.shape))
if variant['log_dir']:
params_file = os.path.join(variant['log_dir'], 'params.pkl')
data = joblib.load(params_file)
raise NotImplementedError
else:
start_epoch = 0
net_size = variant['net_size']
qf = NNQFunction(obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size])
policy = TanhMlpPolicy(
obs_dim=obs_dim,
action_dim=action_dim,
hidden_sizes=[net_size, net_size],
)
es = OUStrategy(
action_space=env.action_space,
mu=0,
theta=0.15,
max_sigma=0.3,
min_sigma=0.3,
decay_period=100000,
)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=es,
policy=policy,
)
# Clamp model parameters
qf.clamp_all_params(min=-0.003, max=0.003)
policy.clamp_all_params(min=-0.003, max=0.003)
replay_buffer = SimpleReplayBuffer(
max_size=variant['replay_buffer_size'],
obs_dim=obs_dim,
action_dim=action_dim,
)
algorithm = DDPG(explo_env=env,
policy=policy,
explo_policy=exploration_policy,
qf=qf,
replay_buffer=replay_buffer,
batch_size=BATCH_SIZE,
eval_env=env,
save_environment=False,
**variant['algo_params'])
if ptu.gpu_enabled():
algorithm.cuda()
# algorithm.pretrain(PATH_LENGTH*2)
algorithm.train(start_epoch=start_epoch)
return algorithm