def run(bsuite_id: str) -> str:
"""Runs an A2C agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
obs_spec = env.observation_spec()
action_spec = env.action_spec()
num_actions = env.action_spec().num_values
hidden_sizes = [FLAGS.num_units] * FLAGS.num_hidden_layers
network = actor_critic_rnn.PolicyValueRNN(hidden_sizes, num_actions)
agent = actor_critic_rnn.ActorCriticRNN(
obs_spec=obs_spec,
action_spec=action_spec,
network=network,
optimizer=snt.optimizers.Adam(learning_rate=FLAGS.learning_rate),
max_sequence_length=FLAGS.sequence_length,
td_lambda=FLAGS.td_lambda,
discount=FLAGS.discount,
seed=FLAGS.seed,
)
num_episodes = FLAGS.num_episodes or getattr(env, 'bsuite_num_episodes')
experiment.run(agent=agent,
environment=env,
num_episodes=num_episodes,
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: str) -> str:
"""Runs an A2C agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
num_actions = env.action_spec().num_values
hidden_sizes = [FLAGS.num_units] * FLAGS.num_hidden_layers
network = actor_critic_rnn.PolicyValueRNN(hidden_sizes, num_actions)
agent = actor_critic_rnn.ActorCriticRNN(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
network=network,
optimizer=tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate),
sequence_length=FLAGS.sequence_length,
td_lambda=FLAGS.td_lambda,
agent_discount=FLAGS.agent_discount,
seed=FLAGS.seed,
)
experiment.run(
agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes, # pytype: disable=attribute-error
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: str) -> str:
"""Runs A2C agent on a single bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
hidden_sizes = [FLAGS.num_units] * FLAGS.num_hidden_layers
network = actor_critic.PolicyValueNet(
hidden_sizes=hidden_sizes,
action_spec=env.action_spec(),
)
agent = actor_critic.ActorCritic(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
network=network,
optimizer=snt.optimizers.Adam(learning_rate=FLAGS.learning_rate),
sequence_length=FLAGS.sequence_length,
td_lambda=FLAGS.td_lambda,
discount=FLAGS.discount,
seed=FLAGS.seed,
)
experiment.run(
agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes, # pytype: disable=attribute-error
verbose=FLAGS.verbose)
return bsuite_id
def test_run(self, bsuite_id: str):
env = bsuite.load_from_id(bsuite_id)
agent = actor_critic.default_agent(env.observation_spec(),
env.action_spec())
experiment.run(agent=agent, environment=env, num_episodes=5)
def run_random_agent(bsuite_id, save_path=SAVE_PATH_RAND, overwrite=True):
env = bsuite.load_and_record(bsuite_id, save_path, overwrite=overwrite)
print('bsuite_id={}, settings={}, num_episodes={}'.format(
bsuite_id, sweep.SETTINGS[bsuite_id], env.bsuite_num_episodes))
agent = random.default_agent(obs_spec=env.observation_spec(),
action_spec=env.action_spec())
experiment.run(agent, env, num_episodes=env.bsuite_num_episodes)
def run(bsuite_id: Text) -> Text:
"""Runs a BDQN agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
online_network = dqn.MLP(env.action_spec().num_values)
target_network = dqn.MLP(env.action_spec().num_values)
agent = dqn.Dqn(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
online_network=online_network,
target_network=target_network,
batch_size=FLAGS.batch_size,
discount=FLAGS.discount,
replay_capacity=FLAGS.replay_capacity,
min_replay_size=FLAGS.min_replay_size,
sgd_period=FLAGS.sgd_period,
target_update_period=FLAGS.target_update_period,
optimizer=tf.optimizers.Adam(learning_rate=FLAGS.learning_rate),
seed=FLAGS.seed,
)
experiment.run(
agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes, # pytype: disable=attribute-error
verbose=FLAGS.verbose)
return bsuite_id
def test_run(self, bsuite_id: str):
env = bsuite.load_from_id(bsuite_id)
agent = boot_dqn.default_agent(env.observation_spec(),
env.action_spec(),
num_ensemble=2)
experiment.run(agent=agent, environment=env, num_episodes=5)
def run_agent(bsuite_id, save_path=SAVE_PATH_RAND, overwrite=True):
# Load environment
env = bsuite.load_and_record(bsuite_id, save_path, overwrite=overwrite)
agent = actor_critic_rnn.default_agent(
obs_spec=env.observation_spec(),
action_spec=env.action_spec()
)
experiment.run(agent, env, num_episodes=env.bsuite_num_episodes)
def run_agent(bsuite_id, save_path=SAVE_PATH_RAND, overwrite=True):
# Load environment
env = bsuite.load_and_record(bsuite_id, save_path, overwrite=overwrite)
print('bsuite_id={}, settings={}, num_episodes={}, start={}'.format(
bsuite_id, sweep.SETTINGS[bsuite_id], env.bsuite_num_episodes,
datetime.now().strftime("%H:%M:%S")))
agent = DQNTF2.default_agent(obs_spec=env.observation_spec(),
action_spec=env.action_spec())
experiment.run(agent, env, num_episodes=env.bsuite_num_episodes)
def run(bsuite_id: str) -> str:
"""Runs a DQN agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
action_spec = env.action_spec()
# Define network.
prior_scale = 5.
hidden_sizes = [50, 50]
def network(inputs: jnp.ndarray) -> jnp.ndarray:
"""Simple Q-network with randomized prior function."""
net = hk.nets.MLP([*hidden_sizes, action_spec.num_values])
prior_net = hk.nets.MLP([*hidden_sizes, action_spec.num_values])
x = hk.Flatten()(inputs)
return net(x) + prior_scale * lax.stop_gradient(prior_net(x))
optimizer = optix.adam(learning_rate=1e-3)
agent = boot_dqn.BootstrappedDqn(
obs_spec=env.observation_spec(),
action_spec=action_spec,
network=network,
optimizer=optimizer,
num_ensemble=FLAGS.num_ensemble,
batch_size=128,
discount=.99,
replay_capacity=10000,
min_replay_size=128,
sgd_period=1,
target_update_period=4,
mask_prob=1.0,
noise_scale=0.,
)
num_episodes = FLAGS.num_episodes or getattr(env, 'bsuite_num_episodes')
experiment.run(agent=agent,
environment=env,
num_episodes=num_episodes,
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: Text) -> Text:
"""Runs a BDQN agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
ensemble = boot_dqn.make_ensemble(
num_actions=env.action_spec().num_values,
num_ensemble=FLAGS.num_ensemble,
num_hidden_layers=FLAGS.num_hidden_layers,
num_units=FLAGS.num_units,
prior_scale=FLAGS.prior_scale)
target_ensemble = boot_dqn.make_ensemble(
num_actions=env.action_spec().num_values,
num_ensemble=FLAGS.num_ensemble,
num_hidden_layers=FLAGS.num_hidden_layers,
num_units=FLAGS.num_units,
prior_scale=FLAGS.prior_scale)
agent = boot_dqn.BootstrappedDqn(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
ensemble=ensemble,
target_ensemble=target_ensemble,
batch_size=FLAGS.batch_size,
agent_discount=FLAGS.agent_discount,
replay_capacity=FLAGS.replay_capacity,
min_replay_size=FLAGS.min_replay_size,
sgd_period=FLAGS.sgd_period,
target_update_period=FLAGS.target_update_period,
optimizer=tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate),
mask_prob=FLAGS.mask_prob,
noise_scale=FLAGS.noise_scale,
epsilon_fn=lambda x: FLAGS.epsilon,
seed=FLAGS.seed)
experiment.run(
agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes, # pytype: disable=attribute-error
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: Text) -> Text:
"""Runs the agent against the environment specified by `bsuite_id`."""
# Load the environment; here we opt for CSV logging.
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
# Making the networks.
hidden_units = [FLAGS.num_units] * FLAGS.num_hidden_layers
online_network = snt.Sequential([
snt.BatchFlatten(),
snt.nets.MLP(hidden_units + [env.action_spec().num_values]),
])
target_network = snt.Sequential([
snt.BatchFlatten(),
snt.nets.MLP(hidden_units + [env.action_spec().num_values]),
])
agent = dqn.DQN(
obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
online_network=online_network,
target_network=target_network,
batch_size=FLAGS.batch_size,
discount=FLAGS.discount,
replay_capacity=FLAGS.replay_capacity,
min_replay_size=FLAGS.min_replay_size,
sgd_period=FLAGS.sgd_period,
target_update_period=FLAGS.target_update_period,
optimizer=tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate),
epsilon=FLAGS.epsilon,
seed=FLAGS.seed,
)
experiment.run(
agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes, # pytype: disable=attribute-error
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: str) -> str:
"""Runs a DQN agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
agent = q_learning.QLearning(env)
num_episodes = FLAGS.num_episodes or getattr(env, 'bsuite_num_episodes')
experiment.run(agent=agent,
environment=env,
num_episodes=num_episodes,
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: str) -> str:
"""Runs a DQN agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
# Making the networks.
hidden_units = [FLAGS.num_units] * FLAGS.num_hidden_layers
online_network = snt.Sequential([
snt.Flatten(),
snt.nets.MLP(hidden_units + [env.action_spec().num_values]),
])
target_network = snt.Sequential([
snt.Flatten(),
snt.nets.MLP(hidden_units + [env.action_spec().num_values]),
])
agent = dqn.DQNTF2(
action_spec=env.action_spec(),
online_network=online_network,
target_network=target_network,
batch_size=FLAGS.batch_size,
discount=FLAGS.discount,
replay_capacity=FLAGS.replay_capacity,
min_replay_size=FLAGS.min_replay_size,
sgd_period=FLAGS.sgd_period,
target_update_period=FLAGS.target_update_period,
optimizer=snt.optimizers.Adam(learning_rate=FLAGS.learning_rate),
epsilon=FLAGS.epsilon,
seed=FLAGS.seed,
)
experiment.run(
agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes, # pytype: disable=attribute-error
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: str) -> str:
"""Runs a DQN agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
layers = [stax.Flatten]
for _ in range(FLAGS.num_hidden_layers):
layers.append(stax.Dense(FLAGS.num_units))
layers.append(stax.Relu)
layers.append(stax.Dense(env.action_spec().num_values))
network_init, network = stax.serial(*layers)
_, network_params = network_init(random.PRNGKey(seed=1),
(-1, ) + env.observation_spec().shape)
agent = dqn.DQNJAX(
action_spec=env.action_spec(),
network=network,
parameters=network_params,
batch_size=FLAGS.batch_size,
discount=FLAGS.discount,
replay_capacity=FLAGS.replay_capacity,
min_replay_size=FLAGS.min_replay_size,
sgd_period=FLAGS.sgd_period,
target_update_period=FLAGS.target_update_period,
learning_rate=FLAGS.learning_rate,
epsilon=FLAGS.epsilon,
seed=FLAGS.seed,
)
experiment.run(
agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes, # pytype: disable=attribute-error
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: str) -> str:
"""Runs a DQN agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
agent = dqn.default_agent(env.observation_spec(), env.action_spec())
num_episodes = FLAGS.num_episodes or getattr(env, 'bsuite_num_episodes')
experiment.run(agent=agent,
environment=env,
num_episodes=num_episodes,
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: Text) -> Text:
"""Runs a random agent on a given bsuite environment, logging to CSV."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
agent = random.default_agent(obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
seed=FLAGS.seed)
experiment.run(
agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes, # pytype: disable=attribute-error
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: Text) -> Text:
"""Runs a ISL agent on a given bsuite environment."""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=FLAGS.save_path,
logging_mode=FLAGS.logging_mode,
overwrite=FLAGS.overwrite,
)
# Making the NNs (q, rho and l).
hidden_units = [FLAGS.num_units] * FLAGS.num_hidden_layers
q_network = snt.Sequential([
snt.BatchFlatten(),
snt.nets.MLP(hidden_units + [env.action_spec().num_values])
])
target_q_network = snt.Sequential([
snt.BatchFlatten(),
snt.nets.MLP(hidden_units + [env.action_spec().num_values])
])
rho_network = snt.Sequential([
snt.BatchFlatten(),
snt.nets.MLP(hidden_units + [env.action_spec().num_values])
])
l_network = [[None for _ in range(env.action_spec().num_values)]
for _ in range(FLAGS.l_approximators)]
target_l_network = [[None for _ in range(env.action_spec().num_values)]
for _ in range(FLAGS.l_approximators)]
for k in range(FLAGS.l_approximators):
for a in range(env.action_spec().num_values):
l_network[k][a] = snt.Sequential([
snt.BatchFlatten(),
snt.nets.MLP(hidden_units,
activate_final=True,
initializers={'b': tf.constant_initializer(0)}),
snt.Linear(1, initializers={'b': tf.constant_initializer(0)}),
lambda x:
(FLAGS.max_l - FLAGS.min_l) * tf.math.sigmoid(x) + FLAGS.min_l
])
target_l_network[k][a] = snt.Sequential([
snt.BatchFlatten(),
snt.nets.MLP(hidden_units,
activate_final=True,
initializers={'b': tf.constant_initializer(0)}),
snt.Linear(1, initializers={'b': tf.constant_initializer(0)}),
lambda x:
(FLAGS.max_l - FLAGS.min_l) * tf.math.sigmoid(x) + FLAGS.min_l
])
agent = isl.ISL(obs_spec=env.observation_spec(),
action_spec=env.action_spec(),
q_network=q_network,
target_q_network=target_q_network,
rho_network=rho_network,
l_network=l_network,
target_l_network=target_l_network,
batch_size=FLAGS.batch_size,
discount=FLAGS.agent_discount,
replay_capacity=FLAGS.replay_capacity,
min_replay_size=FLAGS.min_replay_size,
sgd_period=FLAGS.sgd_period,
target_update_period=FLAGS.target_update_period,
optimizer_primal=tf.train.AdamOptimizer(
learning_rate=FLAGS.q_learning_rate),
optimizer_dual=tf.train.AdamOptimizer(
learning_rate=FLAGS.rho_learning_rate),
optimizer_l=tf.train.AdamOptimizer(
learning_rate=FLAGS.l_learning_rate),
learn_iters=FLAGS.learn_iters,
l_approximators=FLAGS.l_approximators,
min_l=FLAGS.min_l,
kappa=FLAGS.kappa,
eta1=FLAGS.eta1,
eta2=FLAGS.eta2,
seed=FLAGS.seed)
experiment.run(agent=agent,
environment=env,
num_episodes=FLAGS.num_episodes or env.bsuite_num_episodes,
verbose=FLAGS.verbose)
return bsuite_id
def run(bsuite_id: str) -> str:
"""
Runs a bsuite experiment and saves the results as csv files
Args:
bsuite_id: string, the id of the bsuite experiment to run
Returns: none
"""
env = bsuite.load_and_record(
bsuite_id=bsuite_id,
save_path=save_path,
logging_mode='csv',
overwrite=True,
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Settings for the neural network
qnet_settings = {
'layers_sizes': [50],
'batch_size': 64,
'noisy_nets': True,
'distributional': True,
'vmin': 0,
'vmax': 1000,
'number_atoms': 51
}
# Settings for the specific agent
settings = {
'batch_size': qnet_settings["batch_size"],
'epsilon_start': 0.0,
'epsilon_decay': 0.00,
'epsilon_min': 0.00,
'gamma': 0.99,
'buffer_size': 2**16,
'lr': 1e-3,
'qnet_settings': qnet_settings,
'start_optimization': 64,
'update_qnet_every': 2,
'update_target_every': 50,
'ddqn': True,
'n_steps': 4,
'duelling_dqn': True,
'prioritized_buffer': True,
'alpha': 0.6,
'beta0': 0.4,
'beta_increment': 1e-6
}
agent = Agent(action_spec=env.action_spec(),
observation_spec=env.observation_spec(),
device=device,
settings=settings)
experiment.run(agent=agent,
environment=env,
num_episodes=env.bsuite_num_episodes,
verbose=False)
return bsuite_id