def run_experiment(**config):
exp_dir = os.getcwd() + '/data/' + EXP_NAME + '/' + config.get(
'exp_name', '')
logger.configure(dir=exp_dir,
format_strs=['stdout', 'log', 'csv'],
snapshot_mode='last')
json.dump(config,
open(exp_dir + '/params.json', 'w'),
indent=2,
sort_keys=True,
cls=ClassEncoder)
env = normalize(config['env'](reset_every_episode=True,
task=config['task']))
dynamics_model = MLPDynamicsModel(
name="dyn_model",
env=env,
learning_rate=config['learning_rate'],
hidden_sizes=config['hidden_sizes'],
valid_split_ratio=config['valid_split_ratio'],
rolling_average_persitency=config['rolling_average_persitency'],
hidden_nonlinearity=config['hidden_nonlinearity'],
batch_size=config['batch_size'],
)
policy = MPCController(
name="policy",
env=env,
dynamics_model=dynamics_model,
discount=config['discount'],
n_candidates=config['n_candidates'],
horizon=config['horizon'],
use_cem=config['use_cem'],
num_cem_iters=config['num_cem_iters'],
)
sampler = Sampler(
env=env,
policy=policy,
num_rollouts=config['num_rollouts'],
max_path_length=config['max_path_length'],
n_parallel=config['n_parallel'],
)
sample_processor = ModelSampleProcessor(recurrent=False)
algo = Trainer(
env=env,
policy=policy,
dynamics_model=dynamics_model,
sampler=sampler,
dynamics_sample_processor=sample_processor,
n_itr=config['n_itr'],
initial_random_samples=config['initial_random_samples'],
dynamics_model_max_epochs=config['dynamic_model_epochs'],
)
algo.train()
def run_experiment(config):
exp_dir = os.getcwd() + '/data/' + EXP_NAME + '/' + config.get('exp_name', '')
logger.configure(dir=exp_dir, format_strs=['stdout', 'log', 'csv'], snapshot_mode='last')
json.dump(config, open(exp_dir + '/params.json', 'w'), indent=2, sort_keys=True, cls=ClassEncoder)
port_policy = 21001
ports = [19999, 20001, 22001,23001,24001]
_env = normalize(config['env'](reset_every_episode=True, task=config['task'], port=port_policy))
dynamics_model = RNNDynamicsModel(
name="dyn_model",
env=_env,
hidden_sizes=config['hidden_sizes'],
learning_rate=config['learning_rate'],
backprop_steps=config['backprop_steps'],
cell_type=config['cell_type'],
batch_size=config['batch_size'],
)
policy = RNNMPCController(
name="policy",
env=_env,
dynamics_model=dynamics_model,
discount=config['discount'],
n_candidates=config['n_candidates'],
horizon=config['horizon'],
use_cem=config['use_cem'],
num_cem_iters=config['num_cem_iters'],
)
sampler = Sampler(
env=_env,
policy=policy,
num_rollouts=config['num_rollouts'],
max_path_length=config['max_path_length'],
n_parallel=config['n_parallel'],
ports=ports
)
sample_processor = ModelSampleProcessor(recurrent=True)
algo = Trainer(
env=_env,
policy=policy,
dynamics_model=dynamics_model,
sampler=sampler,
sample_processor=sample_processor,
n_itr=config['n_itr'],
initial_random_samples=config['initial_random_samples'],
dynamics_model_max_epochs=config['dynamic_model_epochs'],
)
algo.train()
def run_experiment(config):
exp_dir = os.getcwd() + '/data/' + EXP_NAME + '/' + config.get(
'exp_name', '')
logger.configure(dir=exp_dir,
format_strs=['stdout', 'log', 'csv'],
snapshot_mode='last')
json.dump(config,
open(exp_dir + '/params.json', 'w'),
indent=2,
sort_keys=True,
cls=ClassEncoder)
port_policy = 21001
ports = [19999, 20001, 22001, 23001, 24001]
#ports = [25001, 26001]
#env = normalize(config['env'](reset_every_episode=True, task=config['task'], port=19999))
#_envs = [normalize(config['env'](reset_every_episode=True, port=pt)) for pt in ports]
# For the policy & etc
_env = normalize(config['env'](reset_every_episode=True,
task=config['task'],
port=port_policy))
dynamics_model = MetaMLPDynamicsModel(
name="dyn_model",
env=_env,
meta_batch_size=config['meta_batch_size'],
inner_learning_rate=config['inner_learning_rate'],
learning_rate=config['learning_rate'],
hidden_sizes=config['hidden_sizes_model'],
valid_split_ratio=config['valid_split_ratio'],
rolling_average_persitency=config['rolling_average_persitency'],
hidden_nonlinearity=config['hidden_nonlinearity_model'],
batch_size=config['adapt_batch_size'],
)
policy = MPCController(
name="policy",
env=_env,
dynamics_model=dynamics_model,
discount=config['discount'],
n_candidates=config['n_candidates'],
horizon=config['horizon'],
use_cem=config['use_cem'],
num_cem_iters=config['num_cem_iters'],
)
sampler = Sampler(
env=_env,
policy=policy,
n_parallel=config['n_parallel'],
max_path_length=config['max_path_length'],
num_rollouts=config['num_rollouts'],
adapt_batch_size=config[
'adapt_batch_size'], # Comment this out and it won't adapt during rollout
ports=ports)
sample_processor = ModelSampleProcessor(recurrent=True)
algo = Trainer(
env=_env,
policy=policy,
dynamics_model=dynamics_model,
sampler=sampler,
sample_processor=sample_processor,
n_itr=config['n_itr'],
initial_random_samples=config['initial_random_samples'],
dynamics_model_max_epochs=config['dynamic_model_epochs'],
)
algo.train()
