def make_configs(self):
configurator = Configurator('grid')
configurator.fixed('log.dir', 'some path')
configurator.grid('network.lr', [0.1, 0.01, 0.05])
configurator.grid('network.layers', [16, 32])
configurator.grid('env.id', ['CartPole-v1', 'Ant-v2', 'HalfCheetah-v2'])
configs = configurator.make_configs()
return configs
def make_configs(self):
configurator = Configurator('grid')
configurator.fixed('cuda', True)
configurator.grid('network.type', ['VAE', 'ConvVAE'])
configurator.fixed('network.z_dim', 8)
configurator.fixed('train.num_epoch', 100)
configurator.fixed('train.batch_size', 128)
configurator.fixed('eval.batch_size', 128)
configurator.fixed('log.interval', 100)
configurator.fixed('log.dir', 'logs')
list_config = configurator.make_configs()
return list_config
def make_configs(self):
configurator = Configurator('grid')
configurator.fixed('cuda', False) # ES for small net, do not use GPU
configurator.fixed('env.id', 'Pendulum-v0')
configurator.fixed('network.recurrent', True)
configurator.fixed('network.hidden_size', [32])
configurator.fixed('es.algo', 'CMAES')
configurator.fixed('es.popsize', 16)
configurator.fixed('es.mu0', 0.0)
configurator.fixed('es.std0', 0.5)
"""Hyperparameter search, later time
configurator.grid('env.id', ['Pendulum-v0', 'Reacher-v2', 'InvertedPendulum-v2', 'HumanoidStandup-v2'])
configurator.grid('network.hidden_size', [[32], [32, 32], [64, 64]])
configurator.grid('es.popsize', [8, 16, 32, 64])
configurator.grid('es.mu0', [0.0, 0.3, 0.5])
configurator.grid('es.std0', [0.1, 0.5, 1.0])
"""
configurator.fixed('train.num_iteration', 3000)
configurator.fixed('train.N', 5)
configurator.fixed('train.T', 300)
configurator.fixed('log.interval', 100)
configurator.fixed('log.dir', 'logs')
list_config = configurator.make_configs()
return list_config
def make_configs(self):
configurator = Configurator('grid')
configurator.fixed('cuda', True) # whether to use GPU
configurator.fixed('env.id', 'HalfCheetah-v2')
configurator.fixed('env.standardize',
True) # whether to use VecStandardize
configurator.fixed('env.time_aware_obs',
False) # whether to append time step to observation
configurator.fixed('network.recurrent', False)
configurator.fixed('network.hidden_sizes', [64, 64]) # TODO: [64, 64]
configurator.fixed(
'network.independent_V',
False) # share or not for params of policy and value network
configurator.fixed('algo.lr', 3e-4)
configurator.fixed('algo.lr_V', 1e-3)
configurator.fixed('algo.use_lr_scheduler', True)
configurator.fixed('algo.gamma', 0.99)
configurator.fixed('algo.gae_lambda', 0.97)
configurator.fixed('agent.standardize_Q',
False) # whether to standardize discounted returns
configurator.fixed('agent.standardize_adv',
True) # whether to standardize advantage estimates
configurator.fixed('agent.max_grad_norm',
0.5) # grad clipping, set None to turn off
configurator.fixed('agent.entropy_coef', 0.0)
configurator.fixed('agent.value_coef', 0.5)
configurator.fixed('agent.fit_terminal_value', True)
configurator.fixed('agent.terminal_value_coef', 0.1)
configurator.fixed('agent.clip_range',
0.2) # PPO epsilon of ratio clipping
configurator.fixed(
'agent.target_kl', 0.015
) # appropriate KL between new and old policies after an update, for early stopping (Usually small, e.g. 0.01, 0.05)
# only for continuous control
configurator.fixed(
'env.clip_action',
True) # clip sampled action within valid bound before step()
configurator.fixed(
'agent.min_std',
1e-6) # min threshould for std, avoid numerical instability
configurator.fixed('agent.std_style',
'exp') # std parameterization, 'exp' or 'softplus'
configurator.fixed('agent.constant_std',
None) # constant std, set None to learn it
configurator.fixed('agent.std_state_dependent',
False) # whether to learn std with state dependency
configurator.fixed('agent.init_std',
0.5) # initial std for state-independent std
configurator.fixed('train.timestep',
1e6) # either 'train.iter' or 'train.timestep'
configurator.fixed('train.N',
2) # number of trajectories per training iteration
configurator.fixed('train.ratio_T',
1.0) # percentage of max allowed horizon
configurator.fixed('eval.independent', False)
configurator.fixed(
'eval.N', 10
) # number of episodes to evaluate, do not specify T for complete episode
configurator.fixed('train.batch_size', 256)
configurator.fixed('train.num_epochs', 80)
configurator.fixed('log.interval', 10) # logging interval
configurator.fixed('log.dir', 'logs/default') # logging directory
list_config = configurator.make_configs()
return list_config
def make_configs(self):
configurator = Configurator('grid')
configurator.fixed('cuda', False) # whether to use GPU
configurator.fixed('env.id', 'HalfCheetah-v2')
configurator.fixed('env.standardize',
True) # whether to use VecStandardize
configurator.fixed('network.recurrent', True)
configurator.fixed('network.hidden_sizes', [32]) # TODO: [64, 64]
configurator.fixed('algo.lr', 1e-3)
configurator.fixed('algo.use_lr_scheduler', False)
configurator.fixed('algo.gamma', 0.99)
configurator.fixed('agent.standardize_Q',
False) # whether to standardize discounted returns
configurator.fixed('agent.standardize_adv',
False) # whether to standardize advantage estimates
configurator.fixed('agent.max_grad_norm',
0.5) # grad clipping, set None to turn off
configurator.fixed('agent.entropy_coef', 0.01)
configurator.fixed('agent.value_coef', 0.5)
# only for continuous control
configurator.fixed(
'agent.min_std',
1e-6) # min threshould for std, avoid numerical instability
configurator.fixed('agent.std_style',
'exp') # std parameterization, 'exp' or 'softplus'
configurator.fixed('agent.constant_std',
None) # constant std, set None to learn it
configurator.fixed('agent.std_state_dependent',
False) # whether to learn std with state dependency
configurator.fixed('agent.init_std',
0.5) # initial std for state-independent std
configurator.fixed('train.timestep',
1e7) # either 'train.iter' or 'train.timestep'
configurator.fixed('train.N',
10) # number of segments per training iteration
configurator.fixed('train.T', 5) # fixed-length segment rolling
configurator.fixed(
'eval.N', 100
) # number of episodes to evaluate, do not specify T for complete episode
configurator.fixed('log.record_interval',
100) # interval to record the logging
configurator.fixed('log.print_interval',
500) # interval to print the logging to screen
configurator.fixed('log.dir', 'logs') # logging directory
list_config = configurator.make_configs()
return list_config
def make_configs(self):
configurator = Configurator('grid')
configurator.fixed('cuda', False)
configurator.fixed('algo.lr', 7e-4)
configurator.fixed('algo.lr_V', 1e-3)
configurator.fixed('algo.gamma', 0.99)
configurator.fixed('algo.gae_lambda', 0.97)
configurator.fixed('env.count', 20)
configurator.fixed('agent.count', 20)
configurator.fixed('agent.standardize_Q', False)
configurator.fixed('agent.standardize_adv', False)
configurator.fixed('agent.max_grad_norm', 0.5)
configurator.fixed('agent.entropy_coef', 0.01)
configurator.fixed('agent.value_coef', 0.5)
configurator.fixed('agent.fit_terminal_value', False)
configurator.fixed('agent.terminal_value_coef', 0.1)
configurator.fixed('train.iter', 10000)
configurator.fixed('log.interval', 10)
configurator.fixed('log.dir', 'logs-3')
return configurator.make_configs()
def test_configurator():
# Construction invalidity check
with pytest.raises(AssertionError):
Configurator(search_mode='n')
with pytest.raises(AssertionError):
Configurator(search_mode='random', num_sample=None)
# Create a configurator
# Grid search
configurator = Configurator(search_mode='grid')
assert len(configurator.items) == 0
with pytest.raises(AssertionError):
configurator.fixed('seeds', [1, 2, 3])
configurator.fixed('log.dir', 'some path')
assert len(configurator.items) == 1
assert isinstance(configurator.items['log.dir'], list)
assert configurator.items['log.dir'][0] == 'some path'
with pytest.raises(AssertionError):
configurator.fixed('log.dir', 'second')
with pytest.raises(AssertionError):
configurator.grid('log.T', 'must be list, not string')
configurator.grid('network.lr', [1e-2, 5e-3, 1e-4, 5e-4])
configurator.grid('network.layers', [1, 2, 3])
configurator.grid('env.id', ['CartPole-v1', 'Ant-v2'])
configs = configurator.make_configs()
assert len(configs) == 24
# order-preserving check
assert all([list(c.keys()) == ['ID', 'log.dir', 'network.lr', 'network.layers', 'env.id'] for c in configs])
assert all([c['log.dir'] == 'some path' for c in configs])
assert all([c['network.lr'] in [1e-2, 5e-3, 1e-4, 5e-4] for c in configs])
assert all([c['network.layers'] in [1, 2, 3] for c in configs])
assert all([c['env.id'] in ['CartPole-v1', 'Ant-v2'] for c in configs])
# Grid search does not allow methods for random search
with pytest.raises(AssertionError):
configurator.categorical('one', [1, 2])
with pytest.raises(AssertionError):
configurator.uniform('two', 1, 3)
with pytest.raises(AssertionError):
configurator.discrete_uniform('three', 5, 10)
with pytest.raises(AssertionError):
configurator.log_uniform('four', 0.0001, 0.1)
Configurator.print_config(configs[20])
config_dataframe = Configurator.to_dataframe(configs)
config_dataframe = Configurator.dataframe_subset(config_dataframe, 'network.lr', [0.01, 0.005])
config_dataframe = Configurator.dataframe_groupview(config_dataframe, ['env.id', 'network.lr'])
del configurator
del configs
del config_dataframe
# Random search
configurator = Configurator('random', num_sample=20)
assert len(configurator.items) == 0
with pytest.raises(AssertionError):
configurator.fixed('seeds', [1, 2, 3])
with pytest.raises(AssertionError):
configurator.categorical('seeds', [1, 2])
with pytest.raises(AssertionError):
configurator.uniform('seeds', 1, 3)
with pytest.raises(AssertionError):
configurator.discrete_uniform('seeds', 5, 10)
with pytest.raises(AssertionError):
configurator.log_uniform('seeds', 0.0001, 0.1)
with pytest.raises(AssertionError):
configurator.grid('network.layers', [1, 2, 3])
configurator.fixed('log.dir', 'some path')
assert len(configurator.items) == 1
assert isinstance(configurator.items['log.dir'], typing.Generator)
assert next(configurator.items['log.dir']) == 'some path'
with pytest.raises(AssertionError):
configurator.fixed('log.dir', 'second')
configurator.categorical('network.layers', [1, 2, 3])
with pytest.raises(AssertionError):
configurator.categorical('network.layers2', 12) # must be list
assert isinstance(configurator.items['network.layers'], typing.Generator)
configurator.uniform('entropy_coef', 0.1, 2.0)
configurator.discrete_uniform('train.N', 1, 100)
configurator.log_uniform('network.lr', 1e-7, 1e-1)
assert len(configurator.items) == 5
configs = configurator.make_configs()
assert len(configs[0]) == 1+5
assert len(configs) == 20
# order-preserving check
l = ['ID', 'log.dir', 'network.layers', 'entropy_coef', 'train.N', 'network.lr']
assert all([list(c.keys()) == l for c in configs])
assert all([c['log.dir'] == 'some path' for c in configs])
assert all([c['network.layers'] in [1, 2, 3] for c in configs])
assert all([c['entropy_coef'] >= 0.1 and c['entropy_coef'] <= 2.0 for c in configs])
assert all([isinstance(c['train.N'], int) for c in configs])
assert all([c['train.N'] >= 1 and c['train.N'] <= 100 for c in configs])
assert all([isinstance(c['network.lr'], float) for c in configs])
assert all([c['network.lr'] >= 1e-7 and c['network.lr'] <= 1e-1 for c in configs])
Configurator.print_config(configs[11])
config_dataframe = Configurator.to_dataframe(configs)
config_dataframe = Configurator.dataframe_subset(config_dataframe, 'network.layers', [1, 2])
config_dataframe = Configurator.dataframe_groupview(config_dataframe, ['network.layers', 'log.dir'])
config_dataframe