def test_continuous(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=True)
config = Configuration(
batch_size=20,
entropy_penalty=0.01,
loss_clipping=0.1,
epochs=10,
optimizer_batch_size=10,
learning_rate=0.0005,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
])
)
agent = PPOAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x / l >= reward_threshold for x, l in zip(r.episode_rewards[-100:],
r.episode_lengths[-100:]))
runner.run(episodes=2000, episode_finished=episode_finished)
print('PPO agent (continuous): ' + str(runner.episode))
if runner.episode < 2000:
passed += 1
print('PPO agent (continuous) passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_multi(self):
passed = 0
def network_builder(inputs, **kwargs):
layer = layers['dense']
state0 = layer(x=layer(x=inputs['state0'], size=32), size=32)
state1 = layer(x=layer(x=inputs['state1'], size=32), size=32)
return state0 * state1
for _ in xrange(5):
environment = MinimalTest(definition=[False, (False, 2)])
config = Configuration(batch_size=8,
keep_last=True,
learning_rate=0.001,
states=environment.states,
actions=environment.actions,
network=network_builder)
agent = DQNNstepAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 15 or not all(
x / l >= reward_threshold for x, l in zip(
r.episode_rewards[-15:], r.episode_lengths[-15:]))
runner.run(episodes=2000, episode_finished=episode_finished)
print('DQN Nstep agent (multi-state/action): ' +
str(runner.episode))
if runner.episode < 2000:
passed += 1
print(
'DQN Nstep agent (multi-state/action) passed = {}'.format(passed))
self.assertTrue(passed >= 2)
def train(config, network_spec=None):
data_provider = DataProvider(config.db)
env = StockEnvironment(data_provider, config, 0)
agent = overwrite_agent(env, network_spec,
config) if config.overwrite_agent else load_agent(
config, env, network_spec)
mlflow.log_param("agent", "tensorforce.agents.DQNAgent")
for key in config.agent_specs:
mlflow.log_param(key, config.agent_specs[key])
runner = Runner(agent=agent, environment=env)
offset = 20000
num_episodes = 20
step = 0
while data_provider.has_data_key(offset + config.max_step_per_episode):
runner.run(num_episodes=num_episodes)
offset = offset + config.max_step_per_episode
env.offset = offset
agent.save(config.agent_dir, config.agent_name)
if step % 10 == 0:
evaluate(config, data_provider,
offset - config.max_step_per_episode, agent)
step += 1
return agent, env
def test_quickstart(self):
sys.stdout.write('\nQuickstart:\n')
sys.stdout.flush()
# Create an OpenAI-Gym environment
environment = OpenAIGym('CartPole-v1')
# Create the agent
agent = PPOAgent(
states=environment.states(),
actions=environment.actions(),
# Automatically configured network
network='auto',
# Memory sampling most recent experiences, with a capacity of 2500 timesteps
# (6100 > [30 batch episodes] * [200 max timesteps per episode])
memory=6100,
# Update every 10 episodes, with a batch of 30 episodes
update_mode=dict(unit='episodes', batch_size=30, frequency=10),
# PPO optimizer
step_optimizer=dict(type='adam', learning_rate=1e-3),
# PPO multi-step optimization: 10 updates, each based on a third of the batch
subsampling_fraction=0.33,
optimization_steps=10,
# MLP baseline
baseline_mode='states',
baseline=dict(type='network', network='auto'),
# Baseline optimizer
baseline_optimizer=dict(type='multi_step',
optimizer=dict(type='adam',
learning_rate=1e-4),
num_steps=5),
# Other parameters
discount=0.99,
entropy_regularization=1e-2,
gae_lambda=None,
likelihood_ratio_clipping=0.2)
# Initialize the runner
runner = Runner(agent=agent, environment=environment)
# Function handle called after each finished episode
def callback(r):
return float(np.mean(r.episode_rewards[-100:])) <= 180.0
# Start the runner
runner.run(num_episodes=1000,
max_episode_timesteps=200,
callback=callback)
runner.close()
if float(np.mean(runner.episode_rewards[-100:])) <= 180.0:
sys.stdout.write('Test failed, exceeding {} episodes\n'.format(
runner.episode))
sys.stdout.flush()
self.assertTrue(expr=False)
else:
sys.stdout.write('Test passed after {} episodes\n'.format(
runner.episode))
sys.stdout.flush()
self.assertTrue(expr=True)
def test_discrete(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(continuous=False)
config = Configuration(
batch_size=8,
learning_rate=0.001,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32, activation='tanh'),
dict(type='dense', size=32, activation='tanh')
])
)
agent = VPGAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=2000, episode_finished=episode_finished)
print('VPG Agent (discrete): ' + str(runner.episode))
if runner.episode < 2000:
passed += 1
print('VPG discrete agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_multi(self):
passed = 0
def network_builder(inputs):
layer = layers['dense']
state0 = layer(x=layer(x=inputs['state0'], size=32), size=32)
state1 = layer(x=layer(x=inputs['state1'], size=32), size=32)
state2 = layer(x=layer(x=inputs['state2'], size=32), size=32)
state3 = layer(x=layer(x=inputs['state3'], size=32), size=32)
return state0 * state1 * state2 * state3
for _ in xrange(5):
environment = MinimalTest(definition=[
False, (False, 2), (False, (1, 2)), (True, (1, 2))
])
config = Configuration(batch_size=8,
learning_rate=0.001,
states=environment.states,
actions=environment.actions,
network=network_builder)
agent = VPGAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 50 or not all(
x >= 1.0 for x in r.episode_rewards[-50:])
runner.run(episodes=2000, episode_finished=episode_finished)
print('VPG agent (multi-state/action): ' + str(runner.episode))
if runner.episode < 2000:
passed += 1
print('VPG agent (multi-state/action) passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_save_restore(self):
environment_spec = {"float": ()}
environment = create_environment(environment_spec)
network_spec = [dict(type='dense', size=32)]
agent = create_agent(environment, network_spec)
runner = Runner(agent=agent, environment=environment)
runner.run(episodes=100)
model_values = agent.model.session.run(
agent.model.get_variables(include_submodules=True,
include_nontrainable=False))
save_path = agent.model.save(directory=self._tmp_dir_path + "/model")
print("Saved at: %s" % (save_path, ))
runner.close()
agent = create_agent(environment, network_spec)
agent.model.restore(directory="", file=save_path)
restored_model_values = agent.model.session.run(
agent.model.get_variables(include_submodules=True,
include_nontrainable=False))
assert len(model_values) == len(restored_model_values)
assert all([
np.array_equal(v1, v2)
for v1, v2 in zip(model_values, restored_model_values)
])
agent.close()
def test_continuous(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(continuous=True)
config = Configuration(batch_size=8,
cg_iterations=20,
cg_damping=0.001,
line_search_steps=20,
max_kl_divergence=0.05,
states=environment.states,
actions=environment.actions,
network=layered_network_builder(
[dict(type='dense', size=32)]))
agent = TRPOAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=10000, episode_finished=episode_finished)
print('TRPO Agent (continuous): ' + str(runner.episode))
if runner.episode < 10000:
passed += 1
print('passed')
else:
print('failed')
print('TRPO continuous agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_discrete(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=False)
config = Configuration(batch_size=8,
learning_rate=0.0005,
memory_capacity=800,
first_update=80,
target_update_frequency=20,
memory=dict(type='replay',
random_sampling=True),
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
]))
agent = CategoricalDQNAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x / l >= reward_threshold for x, l in zip(
r.episode_rewards[-100:], r.episode_lengths[-100:]))
runner.run(episodes=1000, episode_finished=episode_finished)
print('Categorical DQN agent: ' + str(runner.episode))
if runner.episode < 1000:
passed += 1
print('Categorical DQN agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def main():
env, agent = set_up()
runner = Runner(agent=agent, environment=env)
runner.run(num_episodes=10000)
agent.save(directory="saved_models")
agent.close()
env.close()
def train_and_test(self, agent, early_stop=-1, n_tests=15):
n_train = TIMESTEPS // n_tests
i = 0
runner = Runner(agent=agent, environment=self)
try:
while i <= n_tests:
self.use_dataset(Mode.TRAIN)
runner.run(timesteps=n_train, max_episode_timesteps=n_train)
self.use_dataset(Mode.TEST)
self.run_deterministic(runner, print_results=True)
if early_stop > 0:
advantages = np.array(
self.acc.episode.advantages[-early_stop:])
if i >= early_stop and np.all(advantages > 0):
i = n_tests
i += 1
except KeyboardInterrupt:
# Lets us kill training with Ctrl-C and skip straight to the final test. This is useful in case you're
# keeping an eye on terminal and see "there! right there, stop you found it!" (where early_stop & n_tests
# are the more methodical approaches)
pass
# On last "how would it have done IRL?" run, without getting in the way (no killing on repeats, 0-balance)
print('Running no-kill test-set')
self.use_dataset(Mode.TEST, no_kill=True)
self.run_deterministic(runner, print_results=True)
def test_discrete(self):
passed = 0
# TRPO can occasionally have numerical issues so we allow for 1 in 5 to fail on Travis
for _ in xrange(5):
environment = MinimalTest(continuous=False)
config = Configuration(
batch_size=8,
learning_rate=0.0001,
cg_iterations=20,
cg_damping=0.001,
line_search_steps=20,
max_kl_divergence=0.05,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32, activation='tanh'),
dict(type='dense', size=32, activation='tanh')
])
)
agent = TRPOAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=2000, episode_finished=episode_finished)
print('TRPO Agent (discrete): ' + str(runner.episode))
if runner.episode < 2000:
passed += 1
print('TRPO discrete agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_replay(self):
environment = MinimalTest(definition=[(False, (1, 2))])
config = Configuration(batch_size=8,
learning_rate=0.001,
memory_capacity=50,
memory=dict(type='replay',
random_sampling=True),
first_update=20,
target_update_frequency=10,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
]))
agent = DQNAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x / l >= reward_threshold for x, l in zip(
r.episode_rewards[-100:], r.episode_lengths[-100:]))
runner.run(episodes=1000, episode_finished=episode_finished)
print('Replay memory DQN: ' + str(runner.episode))
def main():
bad_seeds_environment, agent = set_up()
runner = Runner(agent=agent, environment=bad_seeds_environment)
runner.run(num_episodes=10000)
agent.save(directory="saved_models")
bad_seeds_environment.close()
agent.close()
def test_discrete_baseline(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=False)
config = Configuration(batch_size=8,
learning_rate=0.001,
states=environment.states,
actions=environment.actions,
baseline=dict(type="mlp",
sizes=[32, 32],
epochs=5,
update_batch_size=8,
learning_rate=0.01),
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
]))
agent = VPGAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x / l >= 0.9 for x, l in zip(
r.episode_rewards[-100:], r.episode_lengths[-100:]))
runner.run(episodes=1500, episode_finished=episode_finished)
print('VPG agent (discrete): ' + str(runner.episode))
if runner.episode < 1500:
passed += 1
print('VPG agent (discrete) passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_multi(self):
passed = 0
def network_builder(inputs):
layer = layers['dense']
state0 = layer(x=layer(x=inputs['state0'], size=32), size=32)
state1 = layer(x=layer(x=inputs['state1'], size=32), size=32)
return state0 * state1
for _ in xrange(5):
environment = MinimalTest(definition=[False, (False, 2)])
config = Configuration(batch_size=8,
learning_rate=0.001,
memory_capacity=800,
first_update=80,
target_update_frequency=20,
states=environment.states,
actions=environment.actions,
network=network_builder)
agent = CategoricalDQNAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 15 or not all(
x >= 1.0 for x in r.episode_rewards[-15:])
runner.run(episodes=2000, episode_finished=episode_finished)
print('Categorical DQN agent (multi-state/action): ' +
str(runner.episode))
if runner.episode < 2000:
passed += 1
print('Categorical DQN agent (multi-state/action) passed = {}'.format(
passed))
self.assertTrue(passed >= 2)
def test_beta(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=True)
actions = environment.actions
actions['min_value'] = -0.5
actions['max_value'] = 1.5
config = Configuration(batch_size=8,
learning_rate=0.01,
states=environment.states,
actions=actions,
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
]))
agent = VPGAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x / l >= 0.9 for x, l in zip(
r.episode_rewards[-100:], r.episode_lengths[-100:]))
runner.run(episodes=1500, episode_finished=episode_finished)
print('VPG agent (beta): ' + str(runner.episode))
if runner.episode < 1500:
passed += 1
print('VPG agent (beta) passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_continuous(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=True)
config = Configuration(batch_size=8,
learning_rate=0.001,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
]))
agent = VPGAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=1000, episode_finished=episode_finished)
print('VPG agent (continuous): ' + str(runner.episode))
if runner.episode < 1000:
passed += 1
print('VPG agent (continuous) passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_naf_agent(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=True)
config = Configuration(batch_size=8,
learning_rate=0.001,
exploration=dict(type='ornstein_uhlenbeck'),
memory_capacity=800,
first_update=80,
target_update_frequency=20,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
]))
agent = NAFAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=1000, episode_finished=episode_finished)
print('NAF agent: ' + str(runner.episode))
if runner.episode < 1000:
passed += 1
print('NAF agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_discrete(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=False)
config = Configuration(batch_size=8,
keep_last=True,
learning_rate=0.001,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
]))
agent = DQNNstepAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x / l >= reward_threshold for x, l in zip(
r.episode_rewards[-100:], r.episode_lengths[-100:]))
runner.run(episodes=1000, episode_finished=episode_finished)
print('DQN Nstep agent: ' + str(runner.episode))
if runner.episode < 1000:
passed += 1
print('DQN Nstep agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_continuous(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=True)
config = Configuration(
batch_size=8,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32, activation='tanh'),
dict(type='dense', size=32, activation='tanh')
])
)
agent = TRPOAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x / l >= reward_threshold for x, l in zip(r.episode_rewards[-100:],
r.episode_lengths[-100:]))
runner.run(episodes=1000, episode_finished=episode_finished)
print('TRPO agent (continuous): ' + str(runner.episode))
if runner.episode < 1000:
passed += 1
print('TRPO agent (continuous) passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def main(
time_limit=None,
scoring="default",
batch_size=16,
gpu_idx=0,
env_version=1,
seed_count=9,
max_count=10,
out_path=None,
num_episodes=int(3 * 10**3),
):
env, agent = set_up(
time_limit=time_limit,
scoring=scoring,
batch_size=batch_size,
gpu_idx=gpu_idx,
env_version=env_version,
seed_count=seed_count,
max_count=max_count,
out_path=out_path,
)
runner = Runner(agent=agent, environment=env)
runner.run(num_episodes=num_episodes)
if out_path is None:
out_path = Path()
else:
out_path = Path(out_path).expanduser()
agent.save(directory=str(out_path / "saved_models"))
agent.close()
env.close()
def test_discrete(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(continuous=False)
config = Configuration(
batch_size=8,
learning_rate=0.001,
memory_capacity=800,
first_update=80,
repeat_update=4,
target_update_frequency=20,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([dict(type='dense', size=32)])
)
agent = DQNAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=1000, episode_finished=episode_finished)
print('DQN Agent: ' + str(runner.episode))
if runner.episode < 1000:
passed += 1
print('DQN Agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def main():
tensorflow_settings()
bad_seeds_environment = Environment.create(
environment=BadSeeds02,
seed_count=10,
bad_seed_count=3,
history_block=2,
max_episode_timesteps=100,
)
agent = Agent.create(
agent="random",
environment=bad_seeds_environment,
summarizer=dict(
directory="training_data/agent_random_env_02/summaries",
labels="all",
frequency=100, # store values every 100 timesteps
),
)
runner = Runner(agent=agent, environment=bad_seeds_environment)
runner.run(num_episodes=10000)
bad_seeds_environment.close()
agent.close()
def load_agent(
time_limit=None,
scoring="default",
batch_size=16,
gpu_idx=0,
env_version=1,
seed_count=9,
max_count=10,
out_path=None,
):
env, agent = set_up(
time_limit=time_limit,
scoring=scoring,
batch_size=batch_size,
gpu_idx=gpu_idx,
env_version=env_version,
seed_count=seed_count,
max_count=max_count,
out_path=out_path,
)
if out_path is None:
out_path = Path()
else:
out_path = Path(out_path).expanduser()
agent.restore(directory=str(out_path / "saved_models"))
runner = Runner(agent=agent, environment=env)
runner.run(num_episodes=20)
return agent
class Player:
"""Mandatory class with the player methods"""
def __init__(self, name='ppo_agent', load_model=None, env=None):
"""Initialization of an agent"""
self.equity_alive = 0
self.actions = []
self.last_action_in_stage = ''
self.temp_stack = []
self.name = name
self.autoplay = True
self.ppo_agent = None
self.poker_env = Environment.create(environment=env,
max_episode_timesteps=100)
self.runner = None
if load_model:
self.load(load_model)
def load(self, model_name):
print("Loading model...")
self.ppo_agent = Agent.load(directory=model_name, format='hdf5')
def start_step_policy(self, observation):
log.info("Random action")
_ = observation
action = self.poker_env.action_space.sample()
return action
def train(self, model_name, num_ep=500):
print('Training...')
self.runner = Runner(agent='ppo.json',
environment=dict(type=self.poker_env),
num_parallel=5,
remote='multiprocessing')
self.runner.run(num_episodes=num_ep)
self.runner.agent.save(directory=model_name, format='hdf5')
self.runner.close()
def play(self, model_name, num_ep=5):
self.load(model_name)
print('Evaluating...')
self.runner = Runner(agent=self.ppo_agent,
environment=dict(type=self.poker_env))
self.runner.run(num_episodes=num_ep, evaluation=True)
self.runner.close()
def action(self, action_space, observation, info):
_ = observation
_ = info
this_player_action_space = {
Action.FOLD, Action.CHECK, Action.CALL, Action.RAISE_POT,
Action.RAISE_HALF_POT, Action.RAISE_2POT
}
action = this_player_action_space.intersection(set(action_space))
return action
def test_runner_evaluation(self):
states = dict(type='float', shape=(1,))
actions = dict(type='int', shape=(), num_values=3)
agent, environment = self.prepare(name='runner-evaluation', states=states, actions=actions)
runner = Runner(agent=agent, environment=environment)
self.num_evaluations = 0
evaluation_frequency = 3
max_evaluation_timesteps = 2
num_evaluation_iterations = 2
def evaluation_callback(r):
self.num_evaluations += 1
self.assertEqual(r.episode, self.num_evaluations * evaluation_frequency)
self.assertEqual(len(r.evaluation_timesteps), num_evaluation_iterations)
for num_timesteps in r.evaluation_timesteps:
self.assertLessEqual(num_timesteps, max_evaluation_timesteps)
runner.run(
num_episodes=10, evaluation_callback=evaluation_callback,
evaluation_frequency=evaluation_frequency,
max_evaluation_timesteps=max_evaluation_timesteps,
num_evaluation_iterations=num_evaluation_iterations
)
runner.close()
sys.stdout.flush()
self.assertTrue(expr=True)
def test_continuous(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(continuous=True)
config = Configuration(
batch_size=8,
cg_iterations=20,
cg_damping=0.001,
line_search_steps=20,
max_kl_divergence=0.05,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32, activation='tanh'),
dict(type='dense', size=32, activation='tanh')
])
)
agent = TRPOAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=2000, episode_finished=episode_finished)
print('TRPO Agent (continuous): ' + str(runner.episode))
if runner.episode < 2000:
passed += 1
print('TRPO continuous agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_discrete(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(continuous=False)
config = Configuration(batch_size=8,
learning_rate=0.001,
memory_capacity=800,
first_update=80,
repeat_update=4,
target_update_frequency=20,
states=environment.states,
actions=environment.actions,
network=layered_network_builder(
[dict(type='dense', size=32)]))
agent = DQNAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=5000, episode_finished=episode_finished)
print('DQN Agent: ' + str(runner.episode))
if runner.episode < 5000:
passed += 1
print('passed')
else:
print('failed')
print('DQN Agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_discrete(self):
passed = 0
# TRPO can occasionally have numerical issues so we allow for 1 in 5 to fail on Travis
for _ in xrange(5):
environment = MinimalTest(continuous=False)
config = Configuration(batch_size=8,
learning_rate=0.0001,
cg_iterations=20,
cg_damping=0.001,
line_search_steps=20,
max_kl_divergence=0.05,
states=environment.states,
actions=environment.actions,
network=layered_network_builder(
[dict(type='dense', size=32)]))
agent = TRPOAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=2000, episode_finished=episode_finished)
print('TRPO Agent (discrete): ' + str(runner.episode))
if runner.episode < 2000:
passed += 1
print('TRPO discrete agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def main():
bad_seeds_environment = Environment.create(
environment=BadSeeds03, seed_count=10, bad_seed_count=3, max_episode_length=100
)
agent = Agent.create(
agent="a2c",
batch_size=100,
horizon=100, # changed from 20 to 100 for agent_03
exploration=0.05, # changed from 0.01 to 0.05 for agent_03
l2_regularization=0.2, # changed from 0.1 to 0.2 for agent_03
#entropy_regularization=0.2, # turned off for agent_03
variable_noise=0.1, # changed from 0.05 to 0.1 for agent_03
environment=bad_seeds_environment,
summarizer=dict(
directory="training_data/agent_03_env_03/summaries",
# list of labels, or 'all'
labels=["graph", "entropy", "kl-divergence", "losses", "rewards"],
frequency=100, # store values every 100 timesteps
),
saver=dict(
directory='saved_models/agent_03_env_03/checkpoints',
frequency=600 # save checkpoint every 600 seconds (10 minutes)
),
)
runner = Runner(agent=agent, environment=bad_seeds_environment)
for _ in range(10):
runner.run(num_episodes=10000)
runner.run(num_episodes=1000, evaluation=True)
bad_seeds_environment.close()
agent.close()
def train_and_test(self, agent, n_steps, n_tests, early_stop):
test_acc = self.acc.tests
n_steps = n_steps * 10000
test_acc.n_tests = n_tests
test_acc.i = 0
timesteps_each = n_steps // n_tests
runner = Runner(agent=agent, environment=self)
try:
while test_acc.i <= n_tests:
self.use_dataset(Mode.TRAIN)
# max_episode_timesteps not required, since we kill on (cash|value)<0 or max_repeats
runner.run(timesteps=timesteps_each)
self.use_dataset(Mode.TEST)
self.run_deterministic(runner, print_results=True)
if early_stop > 0:
sharpes = np.array(self.acc.episode.sharpes[-early_stop:])
if test_acc.i >= early_stop and np.all(sharpes > 0):
test_acc.i = n_tests
test_acc.i += 1
except KeyboardInterrupt:
# Lets us kill training with Ctrl-C and skip straight to the final test. This is useful in case you're
# keeping an eye on terminal and see "there! right there, stop you found it!" (where early_stop & n_steps
# are the more methodical approaches)
pass
# On last "how would it have done IRL?" run, without getting in the way (no killing on repeats, 0-balance)
print('Running no-kill test-set')
self.use_dataset(Mode.TEST, full_set=True)
self.run_deterministic(runner, print_results=True)
def test_restore_from_checkpoint(self):
saver_steps = 15
steps_per_episode = 20
train_episodes = 2
assert ((steps_per_episode + 1) * train_episodes % saver_steps) > 0
environment = DummyEnv()
network_spec = [
dict(type='dense', size=4)
]
model_path = self._tmp_dir_path + "/model_auto_save"
saver_spec = dict(
directory=model_path,
steps=saver_steps,
load=False
)
agent = create_agent(environment, network_spec, saver_spec)
runner = Runner(agent=agent, environment=environment)
runner.run(max_episode_timesteps=steps_per_episode, episodes=train_episodes)
# Deliberately avoid closing the runner/agent to simulate unexpected shutdown
agent = create_agent(environment, network_spec)
agent.restore_model(directory=model_path)
agent.reset()
expected_timestep = train_episodes * (steps_per_episode + 1) // saver_steps * saver_steps
assert agent.episode == train_episodes - 1
assert agent.timestep == expected_timestep
runner = Runner(agent=agent, environment=environment)
runner.run(max_episode_timesteps=steps_per_episode, episodes=train_episodes)
assert agent.episode == 2 * train_episodes - 1
runner.close()
def main():
parser = argparse.ArgumentParser(description="Train an IBM agent")
parser.add_argument("--render",
default=False,
action='store_true',
help="Whether to render or not. Defaults to False.")
args = parser.parse_args()
for n_simple in [3]: #[1, 2, 3]:
agent, environment = make_agent_env(1, n_simple, args.render)
agent = restore_agent(agent)
# Run
runner = Runner(agent=agent, environment=environment)
while True:
runner.run(episodes=100, max_episode_timesteps=2000)
ave_reward = np.mean(runner.episode_rewards)
print("Average reward: %f with %d SimpleAgents" %
(ave_reward, n_simple))
directory = os.path.join(os.getcwd(), "log", "agent")
runner.agent.save_model(directory=directory)
if ave_reward > 0 and n_simple < 3:
break
if ave_reward > 0.9:
break
try:
runner.close()
except AttributeError as e:
pass
def test_naf_agent(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(continuous=True)
config = Configuration(
batch_size=8,
learning_rate=0.001,
exploration=dict(type='ornstein_uhlenbeck'),
memory_capacity=800,
first_update=80,
repeat_update=4,
target_update_frequency=20,
clip_gradients=1.0,
states=environment.states,
actions=environment.actions,
network=layered_network_builder([dict(type='dense', size=32)])
# batch_size=8,
# learning_rate=0.0025,
# # exploration="OrnsteinUhlenbeckProcess",
# # exploration_kwargs=dict(
# # sigma=0.1,
# # mu=0,
# # theta=0.1
# # ),
# discount=0.99,
# memory_capacity=800,
# first_update=80,
# repeat_update=4,
# target_update_frequency=20,
# states=environment.states,
# actions=environment.actions,
# clip_gradients=5.0,
# network=layered_network_builder([dict(type='dense', size=32), dict(type='dense', size=32)])
)
agent = NAFAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=2000, episode_finished=episode_finished)
print('NAF Agent: ' + str(runner.episode))
if runner.episode < 2000:
passed += 1
print('NAF Agent passed = {}'.format(passed))
self.assertTrue(passed >= 3)
def test_dqfd_agent(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(continuous=False)
config = Configuration(
batch_size=16,
learning_rate=0.001,
memory_capacity=800,
first_update=80,
repeat_update=4,
target_update_frequency=20,
demo_memory_capacity=100,
demo_sampling_ratio=0.1,
states=environment.states,
actions=environment.actions,
network=layered_network_builder(layers_config=[dict(type='dense', size=32, l2_regularization=0.0001)])
)
agent = DQFDAgent(config=config)
# First generate demonstration data and pretrain
demonstrations = list()
terminal = True
for n in xrange(50):
if terminal:
state = environment.reset()
action = 1
state, reward, terminal = environment.execute(action=action)
demonstration = dict(state=state, action=action, reward=reward, terminal=terminal, internal=[])
demonstrations.append(demonstration)
agent.import_demonstrations(demonstrations)
agent.pretrain(steps=1000)
# Normal training
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(x >= 1.0 for x in r.episode_rewards[-100:])
runner.run(episodes=1000, episode_finished=episode_finished)
print('DQFD Agent: ' + str(runner.episode))
if runner.episode < 1000:
passed += 1
print('DQFD Agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_example(self):
passed = 0
for _ in xrange(3):
# Create an OpenAIgym environment
env = OpenAIGym('CartPole-v0')
# Create a Trust Region Policy Optimization agent
agent = TRPOAgent(config=Configuration(
loglevel='info',
batch_size=100,
baseline='mlp',
baseline_args=None,
baseline_kwargs=dict(
size=32,
repeat_update=100
),
override_line_search=False,
generalized_advantage_estimation=True,
normalize_advantage=False,
gae_lambda=0.97,
cg_iterations=20,
cg_damping=0.01,
line_search_steps=20,
max_kl_divergence=0.005,
states=env.states,
actions=env.actions,
network=layered_network_builder([
dict(type='dense', size=32, activation='tanh'),
dict(type='dense', size=32, activation='tanh')
])
))
runner = Runner(agent=agent, environment=env)
def episode_finished(r):
# Test if mean reward over 50 should ensure that learning took off
avg_reward = np.mean(r.episode_rewards[-50:])
return r.episode < 100 or avg_reward < 50.0
runner.run(episodes=2000, max_timesteps=200, episode_finished=episode_finished)
if runner.episode < 2000:
passed += 1
print('Quick start example passed = {}'.format(passed))
self.assertTrue(passed >= 2)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="ID of the gym environment")
parser.add_argument('-a', '--agent', default='DQNAgent')
parser.add_argument('-c', '--agent-config', help="Agent configuration file")
parser.add_argument('-n', '--network-config', help="Network configuration file")
parser.add_argument('-e', '--episodes', type=int, default=50000, help="Number of episodes")
parser.add_argument('-t', '--max-timesteps', type=int, default=2000*60, help="Maximum number of timesteps per episode")
# parser.add_argument('-m', '--monitor', help="Save results to this directory")
# parser.add_argument('-ms', '--monitor-safe', action='store_true', default=False, help="Do not overwrite previous results")
# parser.add_argument('-mv', '--monitor-video', type=int, default=0, help="Save video every x steps (0 = disabled)")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se', '--save-episodes', type=int, default=100, help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('-D', '--debug', action='store_true', default=False, help="Show debug outputs")
args = parser.parse_args()
env = OpenAIUniverse(args.gym_id)
env.configure(remotes=1)
default = dict(
repeat_actions=1,
actions=env.actions,
states=env.states,
max_episode_length=args.max_timesteps
)
if args.agent_config:
config = Configuration.from_json(args.agent_config)
else:
config = Configuration()
config.default(default)
if args.network_config:
network_config = Configuration.from_json(args.network_config).network_layers
else:
if config.network_layers:
network_config = config.network_layers
else:
raise TensorForceError("Error: No network configuration provided.")
if args.debug:
print("Configuration:")
print(config)
logger = logging.getLogger(__name__)
logger.setLevel(log_levels[config.loglevel])
stack = None
agent = create_agent(args.agent, config, network_config)
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError("Could not load agent from {}: No such directory.".format(load_dir))
agent.load_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(config)
runner = Runner(agent, env, preprocessor=stack, repeat_actions=config.repeat_actions)
if args.save:
save_dir = os.path.dirname(args.save)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError("Cannot save agent to dir {} ()".format(save_dir))
runner.save_model(args.save, args.save_episodes)
report_episodes = args.episodes // 1000
if args.debug:
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
logger.info("Finished episode {ep} after {ts} timesteps".format(ep=r.episode, ts=r.timestep))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(np.mean(r.episode_rewards[-500:])))
logger.info("Average of last 100 rewards: {}".format(np.mean(r.episode_rewards[-100:])))
return True
logger.info("Starting {agent} for Environment '{env}'".format(agent=agent, env=env))
runner.run(args.episodes, args.max_timesteps, episode_finished=episode_finished)
logger.info("Learning finished. Total episodes: {ep}".format(ep=runner.episode))
if args.monitor:
env.gym.monitor.close()
env.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="ID of the gym environment")
parser.add_argument('-a', '--agent', help='Agent')
parser.add_argument('-c', '--agent-config', help="Agent configuration file")
parser.add_argument('-n', '--network-config', help="Network configuration file")
parser.add_argument('-e', '--episodes', type=int, default=50000, help="Number of episodes")
parser.add_argument('-t', '--max-timesteps', type=int, default=2000, help="Maximum number of timesteps per episode")
parser.add_argument('-m', '--monitor', help="Save results to this directory")
parser.add_argument('-ms', '--monitor-safe', action='store_true', default=False, help="Do not overwrite previous results")
parser.add_argument('-mv', '--monitor-video', type=int, default=0, help="Save video every x steps (0 = disabled)")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se', '--save-episodes', type=int, default=100, help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('-D', '--debug', action='store_true', default=False, help="Show debug outputs")
args = parser.parse_args()
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG) # configurable!!!
environment = OpenAIGym(args.gym_id, monitor=args.monitor, monitor_safe=args.monitor_safe, monitor_video=args.monitor_video)
if args.agent_config:
agent_config = Configuration.from_json(args.agent_config)
else:
agent_config = Configuration()
logger.info("No agent configuration provided.")
if args.network_config:
network = from_json(args.network_config)
else:
network = None
logger.info("No network configuration provided.")
agent_config.default(dict(states=environment.states, actions=environment.actions, network=network))
agent = agents[args.agent](config=agent_config)
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError("Could not load agent from {}: No such directory.".format(load_dir))
agent.load_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent_config)
if args.save:
save_dir = os.path.dirname(args.save)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError("Cannot save agent to dir {} ()".format(save_dir))
runner = Runner(
agent=agent,
environment=environment,
repeat_actions=1,
save_path=args.save,
save_episodes=args.save_episodes
)
report_episodes = args.episodes // 1000
if args.debug:
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
logger.info("Finished episode {ep} after {ts} timesteps".format(ep=r.episode, ts=r.timestep))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(sum(r.episode_rewards[-500:]) / 500))
logger.info("Average of last 100 rewards: {}".format(sum(r.episode_rewards[-100:]) / 100))
return True
logger.info("Starting {agent} for Environment '{env}'".format(agent=agent, env=environment))
runner.run(args.episodes, args.max_timesteps, episode_finished=episode_finished)
logger.info("Learning finished. Total episodes: {ep}".format(ep=runner.episode))
if args.monitor:
environment.gym.monitor.close()
environment.close()
line_search_steps=20,
max_kl_divergence=0.005,
gamma=0.97,
continuous=False,
preprocessing=None,
states=env.states,
actions=env.actions,
network=layered_network_builder([dict(type='dense', size=32, activation='tanh'),
dict(type='dense', size=32, activation='tanh')])
))
# Create the runner
runner = Runner(agent=agent, environment=env)
# Callback function printing episode statistics
def episode_finished(r):
print("Finished episode {ep} after {ts} timesteps (reward: {reward})".format(ep=r.episode, ts=r.timestep,
reward=r.episode_rewards[-1]))
return True
# Start learning
runner.run(episodes=3000, max_timesteps=200, episode_finished=episode_finished)
# Print statistics
print("Learning finished. Total episodes: {ep}. Average reward of last 100 episodes: {ar}.".format(ep=runner.episode,
ar=np.mean(
runner.episode_rewards[
-100:])))
def main():
parser = argparse.ArgumentParser(description="Playground Flags.")
parser.add_argument("--game",
default="pommerman",
help="Game to choose.")
parser.add_argument("--config",
default="PommeFFA-v0",
help="Configuration to execute. See env_ids in "
"configs.py for options.")
parser.add_argument("--agents",
default="tensorforce::ppo,test::agents.SimpleAgent,"
"test::agents.SimpleAgent,test::agents.SimpleAgent",
help="Comma delineated list of agent types and docker "
"locations to run the agents.")
parser.add_argument("--agent_env_vars",
help="Comma delineated list of agent environment vars "
"to pass to Docker. This is only for the Docker Agent."
" An example is '0:foo=bar:baz=lar,3:foo=lam', which "
"would send two arguments to Docker Agent 0 and one to"
" Docker Agent 3.",
default="")
parser.add_argument("--record_pngs_dir",
default=None,
help="Directory to record the PNGs of the game. "
"Doesn't record if None.")
parser.add_argument("--record_json_dir",
default=None,
help="Directory to record the JSON representations of "
"the game. Doesn't record if None.")
parser.add_argument("--render",
default=True,
help="Whether to render or not. Defaults to True.")
parser.add_argument("--game_state_file",
default=None,
help="File from which to load game state. Defaults to "
"None.")
args = parser.parse_args()
config = args.config
record_pngs_dir = args.record_pngs_dir
record_json_dir = args.record_json_dir
agent_env_vars = args.agent_env_vars
game_state_file = args.game_state_file
# TODO: After https://github.com/MultiAgentLearning/playground/pull/40
# this is still missing the docker_env_dict parsing for the agents.
agents = [
helpers.make_agent_from_string(agent_string, agent_id+1000)
for agent_id, agent_string in enumerate(args.agents.split(","))
]
env = make(config, agents, game_state_file)
training_agent = None
for agent in agents:
if type(agent) == TensorForceAgent:
training_agent = agent
env.set_training_agent(agent.agent_id)
break
if args.record_pngs_dir:
assert not os.path.isdir(args.record_pngs_dir)
os.makedirs(args.record_pngs_dir)
if args.record_json_dir:
assert not os.path.isdir(args.record_json_dir)
os.makedirs(args.record_json_dir)
# Create a Proximal Policy Optimization agent
agent = training_agent.initialize(env)
atexit.register(functools.partial(clean_up_agents, agents))
wrapped_env = WrappedEnv(env, visualize=args.render)
runner = Runner(agent=agent, environment=wrapped_env)
runner.run(episodes=10, max_episode_timesteps=2000)
print("Stats: ", runner.episode_rewards, runner.episode_timesteps,
runner.episode_times)
try:
runner.close()
except AttributeError as e:
pass
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="ID of the gym environment")
parser.add_argument('-a', '--agent', help='Agent')
parser.add_argument('-c', '--agent-config', help="Agent configuration file")
parser.add_argument('-n', '--network-config', help="Network configuration file")
parser.add_argument('-e', '--episodes', type=int, default=50000, help="Number of episodes")
parser.add_argument('-t', '--max-timesteps', type=int, default=2000, help="Maximum number of timesteps per episode")
parser.add_argument('-w', '--num-workers', type=int, default=1, help="Number of worker agents")
parser.add_argument('-m', '--monitor', help="Save results to this file")
parser.add_argument('-M', '--mode', choices=['tmux', 'child'], default='tmux', help="Starter mode")
parser.add_argument('-L', '--logdir', default='logs_async', help="Log directory")
parser.add_argument('-C', '--is-child', action='store_true')
parser.add_argument('-i', '--task-index', type=int, default=0, help="Task index")
parser.add_argument('-K', '--kill', action='store_true', default=False, help="Kill runners")
parser.add_argument('-D', '--debug', action='store_true', default=False, help="Show debug outputs")
args = parser.parse_args()
session_name = 'openai_async'
shell = '/bin/bash'
kill_cmds = [
"kill $( lsof -i:12222-{} -t ) > /dev/null 2>&1".format(12222 + args.num_workers),
"tmux kill-session -t {}".format(session_name),
]
if args.kill:
os.system("\n".join(kill_cmds))
return 0
if not args.is_child:
# start up child processes
target_script = os.path.abspath(inspect.stack()[0][1])
def wrap_cmd(session, name, cmd):
if isinstance(cmd, list):
cmd = ' '.join(shlex_quote(str(arg)) for arg in cmd)
if args.mode == 'tmux':
return 'tmux send-keys -t {}:{} {} Enter'.format(session, name, shlex_quote(cmd))
elif args.mode == 'child':
return '{} > {}/{}.{}.out 2>&1 & echo kill $! >> {}/kill.sh'.format(
cmd, args.logdir, session, name, args.logdir
)
def build_cmd(index):
cmd_args = [
'CUDA_VISIBLE_DEVICES=',
sys.executable, target_script,
args.gym_id,
'--is-child',
'--agent', args.agent,
'--agent-config', os.path.join(os.getcwd(), args.agent_config),
'--network-config', os.path.join(os.getcwd(), args.network_config),
'--num-workers', args.num_workers,
'--task-index', index
]
if args.debug:
cmd_args.append('--debug')
return cmd_args
if args.mode == 'tmux':
cmds = kill_cmds + ['tmux new-session -d -s {} -n ps'.format(session_name)]
elif args.mode == 'child':
cmds = ['mkdir -p {}'.format(args.logdir),
'rm -f {}/kill.sh'.format(args.logdir),
'echo "#/bin/bash" > {}/kill.sh'.format(args.logdir),
'chmod +x {}/kill.sh'.format(args.logdir)]
cmds.append(wrap_cmd(session_name, 'ps', build_cmd(-1)))
for i in xrange(args.num_workers):
name = 'w_{}'.format(i)
if args.mode == 'tmux':
cmds.append('tmux new-window -t {} -n {} -d {}'.format(session_name, name, shell))
cmds.append(wrap_cmd(session_name, name, build_cmd(i)))
# add one PS call
# cmds.append('tmux new-window -t {} -n ps -d {}'.format(session_name, shell))
print("\n".join(cmds))
os.system("\n".join(cmds))
return 0
ps_hosts = ['127.0.0.1:{}'.format(12222)]
worker_hosts = []
port = 12223
for _ in range(args.num_workers):
worker_hosts.append('127.0.0.1:{}'.format(port))
port += 1
cluster = {'ps': ps_hosts, 'worker': worker_hosts}
cluster_spec = tf.train.ClusterSpec(cluster)
environment = OpenAIGym(args.gym_id)
if args.agent_config:
agent_config = Configuration.from_json(args.agent_config)
else:
raise TensorForceError("No agent configuration provided.")
if not args.network_config:
raise TensorForceError("No network configuration provided.")
agent_config.default(dict(states=environment.states, actions=environment.actions, network=from_json(args.network_config)))
agent_config.default(dict(distributed=True, cluster_spec=cluster_spec, global_model=(args.task_index == -1), device=('/job:ps' if args.task_index == -1 else '/job:worker/task:{}/cpu:0'.format(args.task_index))))
logger = logging.getLogger(__name__)
logger.setLevel(log_levels[agent_config.loglevel])
agent = agents[args.agent](config=agent_config)
logger.info("Starting distributed agent for OpenAI Gym '{gym_id}'".format(gym_id=args.gym_id))
logger.info("Config:")
logger.info(agent_config)
runner = Runner(
agent=agent,
environment=environment,
repeat_actions=1,
cluster_spec=cluster_spec,
task_index=args.task_index
)
report_episodes = args.episodes // 1000
if args.debug:
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
logger.info("Finished episode {ep} after {ts} timesteps".format(ep=r.episode, ts=r.timestep))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(sum(r.episode_rewards[-500:]) / 500))
logger.info("Average of last 100 rewards: {}".format(sum(r.episode_rewards[-100:]) / 100))
return True
runner.run(args.episodes, args.max_timesteps, episode_finished=episode_finished)
def main():
parser = argparse.ArgumentParser()
# N.b. if ran from within lab, the working directory is something like lab/bazel-out/../../tensorforce
# Hence, relative paths will not work without first fetching the path of this run file
parser.add_argument('-id', '--level-id', default='tests/demo_map',help="DeepMind Lab level id")
parser.add_argument('-a', '--agent', default='VPGAgent')
parser.add_argument('-c', '--agent-config', help="Agent configuration file")
parser.add_argument('-n', '--network-config', help="Network configuration file")
parser.add_argument('-e', '--episodes', type=int, default=1000, help="Number of episodes")
parser.add_argument('-t', '--max-timesteps', type=int, default=200, help="Maximum number of timesteps per episode")
parser.add_argument('-m', '--monitor', help="Save results to this directory")
parser.add_argument('-ms', '--monitor-safe', action='store_true', default=False, help="Do not overwrite previous results")
parser.add_argument('-mv', '--monitor-video', type=int, default=0, help="Save video every x steps (0 = disabled)")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se', '--save-episodes', type=int, default=100, help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('-D', '--debug', action='store_true', default=True, help="Show debug outputs")
# Redirect output to file
sys.stdout = open('lab_output.txt', 'w')
args = parser.parse_args()
environment = DeepMindLab(args.level_id)
path = os.path.dirname(__file__)
if args.agent_config:
# Use absolute path
agent_config = Configuration.from_json(path + args.agent_config, True)
else:
raise TensorForceError("No agent configuration provided.")
if not args.network_config:
raise TensorForceError("No network configuration provided.")
agent_config.default(dict(states=environment.states, actions=environment.actions,
network=from_json(path + args.network_config, True)))
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO) # configurable!!!
agent = agents[args.agent](config=agent_config)
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError("Could not load agent from {}: No such directory.".format(load_dir))
agent.load_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent_config)
runner = Runner(
agent=agent,
environment=environment,
repeat_actions=1,
save_path=args.save,
save_episodes=args.save_episodes
)
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError("Could not load agent from {}: No such directory.".format(load_dir))
agent.load_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent_config)
if args.save:
save_dir = os.path.dirname(args.save)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError("Cannot save agent to dir {} ()".format(save_dir))
report_episodes = args.episodes // 1000
def episode_finished(r):
if r.episode % report_episodes == 0:
logger.info("Finished episode {ep} after {ts} timesteps".format(ep=r.episode + 1, ts=r.timestep + 1))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(np.mean(r.episode_rewards[-500:])))
logger.info("Average of last 100 rewards: {}".format(np.mean(r.episode_rewards[-100:])))
return True
logger.info("Starting {agent} for Lab environment '{env}'".format(agent=agent, env=environment))
runner.run(args.episodes, args.max_timesteps, episode_finished=episode_finished)
logger.info("Learning finished. Total episodes: {ep}".format(ep=runner.episode + 1))
environment.close()