def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = -np.log(1. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], update_eps=update_eps,
**kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, _ = env.step(env_action)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts
and num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act, debug['q_func'], debug['obs']
def train_policy(arglist):
with U.single_threaded_session():
# Create the environment
if arglist.use_dense_rewards:
print("Will use env MineRLNavigateDense-v0")
env = gym.make("MineRLNavigateDense-v0")
env_name = "MineRLNavigateDense-v0"
else:
print("Will use env MineRLNavigate-v0")
env = gym.make('MineRLNavigate-v0')
env_name = "MineRLNavigate-v0"
if arglist.force_forward:
env = MineCraftWrapperSimplified(env)
else:
env = MineCraftWrapper(env)
if not arglist.use_demonstrations:
# Use stack of last 4 frames as obs
env = FrameStack(env, 4)
# Create all the functions necessary to train the model
act, train, update_target, debug = deepq.build_train(
make_obs_ph=lambda name: ObservationInput(env.observation_space,
name=name),
q_func=build_q_func('conv_only', dueling=True),
num_actions=env.action_space.n,
gamma=0.9,
optimizer=tf.train.AdamOptimizer(learning_rate=5e-4),
)
# Create the replay buffer(s) (TODO: Use prioritized replay buffer)
if arglist.use_demonstrations:
replay_buffer = ReplayBuffer(int(arglist.replay_buffer_len / 2))
demo_buffer = load_demo_buffer(env_name,
int(arglist.replay_buffer_len / 2))
else:
replay_buffer = ReplayBuffer(arglist.replay_buffer_len)
# Create the schedule for exploration starting from 1 (every action is random) down to
# 0.02 (98% of actions are selected according to values predicted by the model).
exploration = LinearSchedule(
schedule_timesteps=arglist.num_exploration_steps *
arglist.num_episodes * arglist.max_episode_steps,
initial_p=1.0,
final_p=arglist.final_epsilon)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
n_episodes = 0
n_steps = 0
obs = env.reset()
log_path = "./learning_curves/minerl_" + str(date.today()) + "_" + str(
time.time()) + ".dat"
log_file = open(log_path, "a")
for episode in range(arglist.num_episodes):
print("Episode: ", str(episode))
done = False
episode_steps = 0
while not done:
# Take action and update exploration to the newest value
action = act(obs[None],
update_eps=exploration.value(n_steps))[0]
new_obs, rew, done, _ = env.step(action)
n_steps += 1
episode_steps += 1
# Break episode
if episode_steps > arglist.max_episode_steps:
done = True
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
# Store rewards
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0)
n_episodes += 1
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if (n_steps > arglist.learning_starts_at_steps) and (n_steps %
4 == 0):
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
32)
train(obses_t, actions, rewards, obses_tp1, dones,
np.ones_like(rewards))
if arglist.use_demonstrations:
if (n_steps < arglist.learning_starts_at_steps) and (
n_steps % 4 == 0):
obses_t, actions, rewards, obses_tp1, dones = demo_buffer.sample(
32)
train(obses_t, actions, rewards, obses_tp1, dones,
np.ones_like(rewards))
if (n_steps > arglist.learning_starts_at_steps) and (
n_steps % 4 == 0):
obses_t, actions, rewards, obses_tp1, dones = demo_buffer.sample(
32)
train(obses_t, actions, rewards, obses_tp1, dones,
np.ones_like(rewards))
# Update target network periodically.
if n_steps % arglist.target_net_update_freq == 0:
update_target()
# Log data for analysis
if done and len(episode_rewards) % 10 == 0:
logger.record_tabular("steps", n_steps)
logger.record_tabular("episodes", len(episode_rewards))
logger.record_tabular(
"mean episode reward",
round(np.mean(episode_rewards[-101:-1]), 1))
logger.record_tabular(
"% time spent exploring",
int(100 * exploration.value(n_steps)))
logger.dump_tabular()
#TODO: Save checkpoints
if n_steps % arglist.checkpoint_rate == 0:
checkpoint_path = "./checkpoints/minerl_" + str(
episode) + "_" + str(date.today()) + "_" + str(
time.time()) + ".pkl"
save_variables(checkpoint_path)
print("%s,%s,%s,%s" %
(n_steps, episode,
round(np.mean(episode_rewards[-101:-1]),
1), int(100 * exploration.value(n_steps))),
file=log_file)
log_file.close()
def __init__(
self,
env,
# observation_space,
# action_space,
network=None,
scope='deepq',
seed=None,
lr=None, # Was 5e-4
lr_mc=5e-4,
total_episodes=None,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=None, # was 0.02
train_freq=1,
train_log_freq=100,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
# checkpoint_path=None,
learning_starts=1000,
gamma=None,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
save_path=None,
load_path=None,
save_reward_threshold=None,
**network_kwargs):
super().__init__(env, seed)
if train_log_freq % train_freq != 0:
raise ValueError(
'Train log frequency should be a multiple of train frequency')
elif checkpoint_freq % train_log_freq != 0:
raise ValueError(
'Checkpoint freq should be a multiple of train log frequency, or model saving will not be logged properly'
)
print('init dqnlearningagent')
self.train_log_freq = train_log_freq
self.scope = scope
self.learning_starts = learning_starts
self.save_reward_threshold = save_reward_threshold
self.batch_size = batch_size
self.train_freq = train_freq
self.total_episodes = total_episodes
self.total_timesteps = total_timesteps
# TODO: scope not doing anything.
if network is None and 'lunar' in env.unwrapped.spec.id.lower():
if lr is None:
lr = 1e-3
if exploration_final_eps is None:
exploration_final_eps = 0.02
#exploration_fraction = 0.1
#exploration_final_eps = 0.02
target_network_update_freq = 1500
#print_freq = 100
# num_cpu = 5
if gamma is None:
gamma = 0.99
network = 'mlp'
network_kwargs = {
'num_layers': 2,
'num_hidden': 64,
}
self.target_network_update_freq = target_network_update_freq
self.gamma = gamma
get_session()
# set_global_seeds(seed)
# TODO: Check whether below is ok to substitue for set_global_seeds.
try:
import tensorflow as tf
tf.set_random_seed(seed)
except ImportError:
pass
self.q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
def make_obs_ph(name):
return ObservationInput(env.observation_space, name=name)
act, self.train, self.train_mc, self.update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=self.q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
optimizer_mc=tf.train.AdamOptimizer(learning_rate=lr_mc),
gamma=gamma,
grad_norm_clipping=10,
param_noise=False,
scope=scope,
# reuse=reuse,
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': self.q_func,
'num_actions': env.action_space.n,
}
self._act = ActWrapper(act, act_params)
self.print_freq = print_freq
self.checkpoint_freq = checkpoint_freq
# Create the replay buffer
self.prioritized_replay = prioritized_replay
self.prioritized_replay_eps = prioritized_replay_eps
if self.prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(
buffer_size,
alpha=prioritized_replay_alpha,
)
if prioritized_replay_beta_iters is None:
if total_episodes is not None:
raise NotImplementedError(
'Need to check how to set exploration based on episodes'
)
prioritized_replay_beta_iters = total_timesteps
self.beta_schedule = LinearSchedule(
prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0,
)
else:
self.replay_buffer = ReplayBuffer(buffer_size)
self.replay_buffer_mc = ReplayBuffer(buffer_size)
self.beta_schedule = None
# Create the schedule for exploration starting from 1.
self.exploration = LinearSchedule(
schedule_timesteps=int(
exploration_fraction *
total_timesteps if total_episodes is None else total_episodes),
initial_p=1.0,
final_p=exploration_final_eps,
)
# Initialize the parameters and copy them to the target network.
U.initialize()
self.update_target()
self.episode_lengths = [0]
self.episode_rewards = [0.0]
self.discounted_episode_rewards = [0.0]
self.start_values = [None]
self.lunar_crashes = [0]
self.lunar_goals = [0]
self.saved_mean_reward = None
self.td = None
if save_path is None:
self.td = tempfile.mkdtemp()
outdir = self.td
self.model_file = os.path.join(outdir, "model")
else:
outdir = os.path.dirname(save_path)
os.makedirs(outdir, exist_ok=True)
self.model_file = save_path
print('DQN agent saving to:', self.model_file)
self.model_saved = False
if tf.train.latest_checkpoint(outdir) is not None:
# TODO: Check scope addition
load_variables(self.model_file, scope=self.scope)
# load_variables(self.model_file)
logger.log('Loaded model from {}'.format(self.model_file))
self.model_saved = True
raise Exception('Check that we want to load previous model')
elif load_path is not None:
# TODO: Check scope addition
load_variables(load_path, scope=self.scope)
# load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
self.train_log_file = None
if save_path and load_path is None:
self.train_log_file = self.model_file + '.log.csv'
with open(self.train_log_file, 'w') as f:
cols = [
'episode',
't',
'td_max',
'td_mean',
'100ep_r_mean',
'100ep_r_mean_discounted',
'100ep_v_mean',
'100ep_n_crashes_mean',
'100ep_n_goals_mean',
'saved_model',
'smoothing',
]
f.write(','.join(cols) + '\n')
self.training_episode = 0
self.t = 0
self.episode_t = 0
"""
n = observation_space.n
m = action_space.n
self.Q = np.zeros((n, m))
self._lr_schedule = lr_schedule
self._eps_schedule = eps_schedule
self._boltzmann_schedule = boltzmann_schedule
"""
# Make placeholder for Q values
self.q_values = debug['q_values']
def __init__(self,
env,
network='mlp',
lr=5e-4,
buffer_size=50000,
exploration_epsilon=0.1,
train_freq=1,
batch_size=32,
learning_starts=1000,
target_network_update_freq=500,
**network_kwargs):
"""DQN wrapper to train option policies
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
lr: float
learning rate for adam optimizer
buffer_size: int
size of the replay buffer
exploration_epsilon: float
value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
learning_starts: int
how many steps of the model to collect transitions for before learning starts
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
network_kwargs
additional keyword arguments to pass to the network builder.
"""
# Creating the network
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.controller_observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.controller_action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
grad_norm_clipping=10,
scope="controller")
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.controller_action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
replay_buffer = ReplayBuffer(buffer_size)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
# Variables that are used during learning
self.act = act
self.train = train
self.update_target = update_target
self.replay_buffer = replay_buffer
self.exp_epsilon = exploration_epsilon
self.train_freq = train_freq
self.batch_size = batch_size
self.learning_starts = learning_starts
self.target_network_update_freq = target_network_update_freq
self.num_actions = env.controller_action_space.n
self.t = 0
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=3000,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=3000,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs
):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=lambda name: ObservationInput(env.observation_space, name=name),
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=0.99,
double_q=False
#grad_norm_clipping=10,
# param_noise=param_noise
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(10000),
initial_p=1.0,
final_p=0.02)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
old_state = None
formula_LTLf_1 = "!d U(g)"
monitoring_RightToLeft = MonitoringSpecification(
ltlf_formula=formula_LTLf_1,
r=0,
c=-0.01,
s=10,
f=-10
)
formula_LTLf_2 = "F(G(bb)) " # break brick
monitoring_BreakBrick = MonitoringSpecification(
ltlf_formula=formula_LTLf_2,
r=10,
c=-0.01,
s=10,
f=0
)
monitoring_specifications = [monitoring_BreakBrick, monitoring_RightToLeft]
def RightToLeftConversion(observation) -> TraceStep:
done=False
global old_state
if arrays_equal(observation[-9:], np.zeros((len(observation[-9:])))): ### Checking if all Bricks are broken
# print('goal reached')
goal = True # all bricks are broken
done = True
else:
goal = False
dead = False
if done and not goal:
dead = True
order = check_ordered(observation[-9:])
if not order:
# print('wrong order', state[5:])
dead=True
done = True
if old_state is not None: # if not the first state
if not arrays_equal(old_state[-9:], observation[-9:]):
brick_broken = True
# check_ordered(state[-9:])
# print(' a brick is broken')
else:
brick_broken = False
else:
brick_broken = False
dictionary={'g': goal, 'd': dead, 'o': order, 'bb':brick_broken}
#print(dictionary)
return dictionary
multi_monitor = MultiRewardMonitor(
monitoring_specifications=monitoring_specifications,
obs_to_trace_step=RightToLeftConversion
)
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
# initialize
done = False
#monitor.get_reward(None, False) # add first state in trace
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
episodeCounter=0
num_episodes=0
for t in itertools.count():
# Take action and update exploration to the newest value
action = act(obs[None], update_eps=exploration.value(t))[0]
#print(action)
#print(action)
new_obs, rew, done, _ = env.step(action)
done=False
#done=False ## FOR FIRE ONLY
#print(new_obs)
#new_obs.append()
start_time = time.time()
rew, is_perm = multi_monitor(new_obs)
#print("--- %s seconds ---" % (time.time() - start_time))
old_state=new_obs
#print(rew)
done=done or is_perm
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
is_solved = t > 100 and np.mean(episode_rewards[-101:-1]) >= 200
if episodeCounter % 100 == 0 or episodeCounter<1:
# Show off the result
#print("coming here Again and Again")
env.render()
if done:
episodeCounter+=1
num_episodes+=1
obs = env.reset()
old_state=None
episode_rewards.append(0)
multi_monitor.reset()
#monitor.get_reward(None, False)
else:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if t > 1000:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(64)
train(obses_t, actions, rewards, obses_tp1, dones, np.ones_like(rewards))
# Update target network periodically.
if t % 1000 == 0:
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
if done and len(episode_rewards) % 10 == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", len(episode_rewards))
logger.record_tabular("currentEpisodeReward", episode_rewards[-1])
logger.record_tabular("mean 100 episode reward", round(np.mean(episode_rewards[-101:-1]), 1))
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
act.save_act()
#save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
# if model_saved:
# if print_freq is not None:
# logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
# load_variables(model_file)
return act
def __init__(self,
env,
gamma,
total_timesteps,
network='mlp',
lr=5e-4,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
learning_starts=1000,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
**network_kwargs):
"""DQN wrapper to train option policies
Parameters
-------
env: gym.Env
environment to train on
gamma: float
discount factor
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
total_timesteps: int
number of env steps to optimizer for
lr: float
learning rate for adam optimizer
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
learning_starts: int
how many steps of the model to collect transitions for before learning starts
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
**network_kwargs
additional keyword arguments to pass to the network builder.
"""
# Adjusting hyper-parameters by considering the number of options policies to learn
num_options = env.get_number_of_options()
buffer_size = num_options * buffer_size
batch_size = num_options * batch_size
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.option_observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
self.num_actions = env.option_action_space.n
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=self.num_actions,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
scope="options")
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': self.num_actions,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(
buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(
exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
# Variables that are used during learning
self.act = act
self.train = train
self.update_target = update_target
self.replay_buffer = replay_buffer
self.beta_schedule = beta_schedule
self.exploration = exploration
self.param_noise = param_noise
self.train_freq = train_freq
self.batch_size = batch_size
self.learning_starts = learning_starts
self.target_network_update_freq = target_network_update_freq
self.prioritized_replay = prioritized_replay
self.prioritized_replay_alpha = prioritized_replay_alpha
self.prioritized_replay_beta0 = prioritized_replay_beta0
self.prioritized_replay_beta_iters = prioritized_replay_beta_iters
self.prioritized_replay_eps = prioritized_replay_eps
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=5,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the trained model from. (default: None)(used in test stage)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
med_libs = MedLibs()
'''Define Q network
inputs: observation place holder(make_obs_ph), num_actions, scope, reuse
outputs(tensor of shape batch_size*num_actions): values of each action, Q(s,a_{i})
'''
q_func = build_q_func(network, **network_kwargs)
''' To put observations into a placeholder '''
# TODO: Can only deal with Discrete and Box observation spaces for now
# observation_space = env.observation_space (default)
# Use sub_obs_space instead
observation_space = med_libs.subobs_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
''' Customize action '''
# TODO: subset of action space.
action_dim = med_libs.sub_act_dim
'''
Returns: deepq.build_train()
act: (tf.Variable, bool, float) -> tf.Variable
function to select and action given observation.
act is computed by [build_act] or [build_act_with_param_noise]
train: (object, np.array, np.array, object, np.array, np.array) -> np.array
optimize the error in Bellman's equation.
update_target: () -> ()
copy the parameters from optimized Q function to the target Q function.
debug: {str: function}
a bunch of functions to print debug data like q_values.
'''
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=action_dim,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
double_q=True,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': action_dim,
}
'''Contruct an act object using ActWrapper'''
act = ActWrapper(act, act_params)
''' Create the replay buffer'''
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
'''Create the schedule for exploration starting from 1.'''
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
'''
Initialize all the uninitialized variables in the global scope and copy them to the target network.
'''
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
sub_obs = med_libs.custom_obs(obs) # TODO: customize observations
pre_obs = obs
reset = True
mydict = med_libs.action_dict
already_starts = False
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
# load_path: a trained model/policy
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
''' Training loop starts'''
t = 0
while t < total_timesteps:
if callback is not None:
if callback(locals(), globals()):
break
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
update_param_noise_threshold = -np.log(1. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
''' Choose action: take action and update exploration to the newest value
'''
# TODO: Mixed action strategy
# Normal status, action is easily determined by rules, use [obs]
action = med_libs.simple_case_action(obs)
# Distraction status, action is determined by Q, with [sub_obs]
if action == -10:
action = act(np.array(sub_obs)[None],
update_eps=update_eps,
**kwargs)[0]
action = med_libs.action_Q_env(
action
) # TODO:action_Q_env, from Q_action(0~2) to env_action(2~4)
reset = False
''' Step action '''
new_obs, rew, done, d_info = env.step(action)
d_att_last = int(pre_obs[0][0])
d_att_now = int(obs[0][0])
d_att_next = int(new_obs[0][0])
''' Store transition in the replay buffer.'''
pre_obs = obs
obs = new_obs
sub_new_obs = med_libs.custom_obs(new_obs)
if (d_att_last == 0 and d_att_now == 1) and not already_starts:
already_starts = True
if already_starts and d_att_now == 1:
replay_buffer.add(sub_obs, action, rew, sub_new_obs,
float(done))
episode_rewards[-1] += rew # Sum of rewards
t = t + 1
print(
'>> Iteration:{}, State[d_att,cd_activate,L4_available,ssl4_activate,f_dc]:{}'
.format(t, sub_obs))
print(
'Dis_Last:{}, Dis_Now:{}, Dis_Next:{},Reward+Cost:{}, Action:{}'
.format(
d_att_last, d_att_now, d_att_next, rew,
list(mydict.keys())[list(
mydict.values()).index(action)]))
# update sub_obs
sub_obs = sub_new_obs
# Done and Reset
if done:
print('Done infos: ', d_info)
print('======= end =======')
obs = env.reset()
sub_obs = med_libs.custom_obs(obs) # TODO: custom obs
pre_obs = obs # TODO: save obs at t-1
already_starts = False
episode_rewards.append(0.0)
reset = True
# Update the Q network parameters
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
# Calculate td-errors
actions = med_libs.action_env_Q(
actions
) # TODO:action_env_Q, from env_action(2~4) to Q_action(0~2)
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically, copy weights of Q to target Q
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts
and num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs
):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = -np.log(1. - exploration.value(t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], update_eps=update_eps, **kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, _ = env.step(env_action)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
load_variables(model_file)
return act
from baselines.deepq.replay_buffer import ReplayBuffer
from baselines.deepq.utils import ObservationInput
from baselines.deepq.models import build_q_func
t_train_time = 2e5
t_test_time = 10000
env = gym.make('CartPole-v0')
action_shape = (1, )
nb_action = 1
observation_shape = (3, )
dataPrimary = pd.read_csv("data_c/Cartpole-v0.csv", header=1)
load_path = 'ddpg_model'
load_path = None
q_func = build_q_func('mlp')
act, train, update_target, debug = deepq.build_train(
make_obs_ph=lambda name: ObservationInput(env.observation_space, name=name
),
q_func=q_func,
num_actions=nb_action,
optimizer=tf.train.AdamOptimizer(learning_rate=5e-4),
)
replay_buffer = ReplayBuffer(50000)
U.initialize()
update_target()
episode_rewards = [0.0]
if load_path is None:
for index, row in dataPrimary.iterrows():
if index > 2:
def main():
# configure logger, disable logging in child MPI processes (with rank > 0)
arg_parser = common_arg_parser()
args, unknown_args = arg_parser.parse_known_args()
extra_args = parse_cmdline_kwargs(unknown_args)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
rank = 0
logger.configure()
else:
logger.configure(format_strs=[])
rank = MPI.COMM_WORLD.Get_rank()
model, env, debug = train(args, extra_args) # Get the trained model
env.close()
if args.save_path is not None and rank == 0:
save_path = osp.expanduser(args.save_path)
model.save(save_path)
if args.adv_alg: # If attack is applied, build the function for crafting adversarial observations
g = tf.Graph()
with g.as_default():
with tf.Session() as sess:
q_func = build_q_func(network='conv_only')
craft_adv_obs = build_adv(
make_obs_tf=lambda name: ObservationInput(env.observation_space, name=name),
q_func=q_func, num_actions=env.action_space.n, epsilon=args.epsilon,
attack=args.adv_alg
)
if args.save_info: # Save all the information in a csv filter
name = args.info_name
csv_file = open('/Users/harry/Documents/info/' + name, mode='a' )
fieldnames = ['episode', 'diff_type', 'diff', 'epsilon', 'steps', 'attack rate', 'success rate', 'score']
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
writer.writeheader()
if args.play:
logger.log("Running trained model")
env = build_env(args)
obs = env.reset()
action_meanings = env.unwrapped.get_action_meanings()
def initialize_placeholders(nlstm=128,**kwargs):
return np.zeros((args.num_env or 1, 2*nlstm)), np.zeros((1))
state, dones = initialize_placeholders(**extra_args)
num_episodes = 0
num_moves = 0
num_success_attack = 0
num_attack = 0
step = 0
q_value_dict = {}
old_diff = 0
diff_type = args.diff_type
print("Type of diff: {}. Threshold to launch attack: {}".format(diff_type, args.diff))
print('-------------------------Episode 0 -------------------------')
while True:
step = step + 1 # Overall steps. Does not reset to 0 when an episode ends
num_moves = num_moves + 1
q_values = debug['q_values']([obs])
q_values = np.squeeze(q_values)
minus_diff = np.max(q_values) - np.min(q_values)
div_diff = np.max(q_values) / np.min(q_values)
sec_ord_diff = minus_diff - old_diff
old_diff = minus_diff
if args.save_q_value: # Save the q value to a file
with open('/Users/harry/Documents/q_value_pong_ep' + str(num_episodes+1) + '_diff' + str(args.diff) + '.csv', 'a') as q_value_file:
q_value_writter = csv.writer(q_value_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
q_value_writter.writerow(q_values)
if args.adv_alg:
diff = minus_diff if args.diff_type == 'diff' else div_diff \
if args.diff_type == 'div_diff' else sec_ord_diff \
if args.diff_type == 'sec_ord_diff' else minus_diff
if diff >= args.diff:
num_attack = num_attack + 1
with g.as_default():
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
adv_obs = craft_adv_obs([obs])[0] # Get the adversarial observation
adv_obs = np.rint(adv_obs)
adv_obs = adv_obs.astype(np.uint8)
if args.preview_image: # Show a few adversarial images on the screen
if num_attack >= 2 and num_attack <= 10:
adv_img = Image.fromarray(np.asarray(adv_obs[:,:,0]), mode='L')
adv_img.show()
if args.save_image: # Save one episode of adversarial images in a folder
if num_episodes == 0:
img = Image.fromarray(np.asarray(adv_obs[:,:,0]), mode='L')
img.save('/Users/harry/Documents/adv_19_99/adv_image_' + str(num_moves) + '.png')
prev_state = np.copy(state)
action, _, _, _ = model.step(obs,S=prev_state, M=dones)
adv_action, _, state, _ = model.step(adv_obs,S=prev_state, M=dones)
if (adv_action != action): # Count as a successful atttack
# print('Action before: {}, Action after: {}'.format(
# action_meanings[action[0]], action_meanings[adv_action[0]]))
num_success_attack = num_success_attack + 1
obs, rew, done, info = env.step(adv_action)
else:
action, _, state, _ = model.step(obs,S=state, M=dones)
obs, rew, done, info = env.step(action)
if args.save_image:
img = Image.fromarray(np.asarray(obs[:,:,0]), mode='L')
img.save('/Users/harry/Documents/adv_images_ep' + str(num_episodes+1) + '/' + str(num_moves) + '.png')
else:
if args.save_image: # Save one episode of normal images in a folder
if num_episodes == 0:
img = Image.fromarray(np.asarray(obs[:,:,0]), mode='L')
img.save('/Users/harry/Documents/normal_obs' + str(num_moves) + '.png')
action, _, state, _ = model.step(obs,S=state, M=dones)
obs, _, done, info = env.step(action)
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
npc_score = info['episode']['r']
score = 21 if npc_score < 0 else 21 - npc_score
obs = env.reset()
print('Episode {} takes {} time steps'.format(num_episodes, num_moves))
print('NPC Score: {}'.format(npc_score))
if args.adv_alg:
attack_rate = float(num_attack) / num_moves
success_rate = float(num_success_attack) / num_attack
print('Percentage of attack: {}'.format(100 * attack_rate))
print('Percentage of successful attacks: {}'.format(100 * success_rate))
info_dict = {'episode': num_episodes+1,'diff_type': args.diff_type, 'diff': args.diff, 'epsilon': args.epsilon,
'steps': num_moves, 'attack rate': attack_rate, 'success rate': success_rate, 'score': score}
writer.writerow(info_dict)
num_moves = 0
num_transfer = 0
num_episodes = num_episodes + 1
num_attack = 0
num_success_attack = 0
print(f'-------------------------Episode {num_episodes}-------------------------')
env.close()
def train_dqn(opts,
seed=None,
lr=1e-3,
total_timesteps=500000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
checkpoint_freq=500000,
learning_starts=1000,
gamma=1.000,
target_network_update_freq=3000,
load_path=None):
"""
Runs the main recorder by binding certain discrete actions to keys.
"""
if os.path.exists(opts.model_dir):
print('Path already exists. Remove? y for yes')
input_char = getch.getch()
if not input_char == 'y':
print('Exiting')
return
shutil.rmtree(opts.model_dir)
os.makedirs(opts.model_dir)
os.makedirs(os.path.join(opts.model_dir, 'logs'))
os.makedirs(os.path.join(opts.model_dir, 'weights'))
#env = gym.make('MountainCar-v0')
env = gym.make('LunarLander-v2')
env._max_episode_steps = 1200
sess = get_session()
set_global_seeds(seed)
train_writer = tf.summary.FileWriter(os.path.join(opts.model_dir, 'logs'),
sess.graph)
q_func = build_q_func('mlp')
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10)
replay_buffer = ReplayBuffer(buffer_size)
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
obs = env.reset()
for t in range(total_timesteps):
# Take action and update exploration to the newest value
env.render()
update_eps = exploration.value(t)
action = act(np.array(obs)[None], update_eps=update_eps)[0]
new_obs, rew, done, _ = env.step(action)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
print("Exploration value: {}".format(exploration.value(t)))
print("Last 25 episode rewards: {}".format(episode_rewards[-25:]))
reward_summary = tf.Summary(value=[
tf.Summary.Value(tag='reward',
simple_value=episode_rewards[-1])
])
train_writer.add_summary(reward_summary, t)
obs = env.reset()
episode_rewards.append(0.0)
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors, summary = train(obses_t, actions, rewards, obses_tp1,
dones, weights)
train_writer.add_summary(summary, t)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
if t > learning_starts and t % checkpoint_freq == 0:
save_variables(
os.path.join(opts.model_dir, 'weights', '{}.model'.format(t)))
save_variables(os.path.join(opts.model_dir, 'weights', 'last.model'))
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=3000,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=3000,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs
):
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=lambda name: ObservationInput(env.observation_space, name=name),
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
# gamma=gamma,
# grad_norm_clipping=10,
# param_noise=param_noise
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(100000),
initial_p=1.0,
final_p=0.02)
# Initialize the paramete print(type(act))rs and copy them to the target network.
U.initialize()
update_target()
old_state = None
formula_LTLf_1 = "!F(die)"
monitoring_RightToLeft = MonitoringSpecification(
ltlf_formula=formula_LTLf_1,
r=1,
c=-10,
s=1,
f=-10
)
monitoring_specifications = [monitoring_RightToLeft]
stepCounter = 0
done = False
def RightToLeftConversion(observation) -> TraceStep:
print(stepCounter)
if(done and not(stepCounter>=199)):
die=True
else:
die=False
dictionary={'die': die}
print(dictionary)
return dictionary
multi_monitor = MultiRewardMonitor(
monitoring_specifications=monitoring_specifications,
obs_to_trace_step=RightToLeftConversion
)
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
episodeCounter=0
num_episodes=0
for t in itertools.count():
# Take action and update exploration to the newest value
action = act(obs[None], update_eps=exploration.value(t))[0]
#print(action)
new_obs, rew, done, _ = env.step(action)
stepCounter+=1
rew, is_perm = multi_monitor(new_obs)
old_state=new_obs
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
is_solved = t > 100 and np.mean(episode_rewards[-101:-1]) >= 200
if episodeCounter % 100 == 0 or episodeCounter<1:
# Show off the result
#print("coming here Again and Again")
env.render()
if done:
episodeCounter+=1
num_episodes+=1
obs = env.reset()
episode_rewards.append(0)
multi_monitor.reset()
stepCounter=0
else:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if t > 1000:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(32)
train(obses_t, actions, rewards, obses_tp1, dones, np.ones_like(rewards))
# Update target network periodically.
if t % 1000 == 0:
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
if done and len(episode_rewards) % 10 == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", len(episode_rewards))
logger.record_tabular("mean 100 episode reward", round(np.mean(episode_rewards[-101:-1]), 1))
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 500 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
act.save_act()
#save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
# if model_saved:
# if print_freq is not None:
# logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
# load_variables(model_file)
return act
def learn(env,
network,
seed=None,
lr=1e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
multiplayer=False,
callback=None,
load_path=None,
load_path_1=None,
load_path_2=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# This was all handled in not the most elegant way
# Variables have a _1 or _2 appended to them to separate them
# and a bunch of if statementss to have the _2 variables not do anything in single-player
# when in multiplayer Space Invaders, need to not reward players for other player dying
isSpaceInvaders = False
if "SpaceInvaders" in str(env):
isSpaceInvaders = True
# put a limit on the amount of memory used, otherwise TensorFlow will consume nearly everything
# this leaves 1 GB free on my computer, others may need to change it
# Create all the functions necessary to train the model
# Create two separate TensorFlow sessions
graph_1 = tf.Graph()
sess_1 = tf.Session(graph=graph_1)
if multiplayer:
graph_2 = tf.Graph()
sess_2 = tf.Session(graph=graph_2)
else:
# set session 2 to None if it's not being used
sess_2 = None
set_global_seeds(seed)
# specify the q functions as separate objects
q_func_1 = build_q_func(network, **network_kwargs)
if multiplayer:
q_func_2 = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
# build everything for the first model
# pass in the session and the "_1" suffix
act_1, train_1, update_target_1, debug_1 = deepq.build_train(
sess=sess_1,
make_obs_ph=make_obs_ph,
q_func=q_func_1,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
scope="deepq")
# a lot of if multiplayer statements duplicating these actions for a second network
# pass in session 2 and "_2" instead
if multiplayer:
act_2, train_2, update_target_2, debug_2 = deepq.build_train(
sess=sess_2,
make_obs_ph=make_obs_ph,
q_func=q_func_2,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise,
scope="deepq")
# separate act_params for each wrapper
act_params_1 = {
'make_obs_ph': make_obs_ph,
'q_func': q_func_1,
'num_actions': env.action_space.n,
}
if multiplayer:
act_params_2 = {
'make_obs_ph': make_obs_ph,
'q_func': q_func_2,
'num_actions': env.action_space.n,
}
# make the act wrappers
act_1 = ActWrapper(act_1, act_params_1)
if multiplayer:
act_2 = ActWrapper(act_2, act_params_2)
# I need to return something if it's single-player
else:
act_2 = None
# Create the replay buffer
# separate replay buffers are required for each network
# this is required for competitive because the replay buffers hold rewards
# and player 2 has different rewards than player 1
if prioritized_replay:
replay_buffer_1 = PrioritizedReplayBuffer(
buffer_size, alpha=prioritized_replay_alpha)
if multiplayer:
replay_buffer_2 = PrioritizedReplayBuffer(
buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer_1 = ReplayBuffer(buffer_size)
if multiplayer:
replay_buffer_2 = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
# initialize both sessions
U.initialize(sess_1)
if multiplayer:
U.initialize(sess_2)
# the session was passed into these functions when they were created
# the separate update functions work within the different sessions
update_target_1()
if multiplayer:
update_target_2()
# keep track of rewards for both models separately
episode_rewards_1 = [0.0]
saved_mean_reward_1 = None
if multiplayer:
episode_rewards_2 = [0.0]
saved_mean_reward_2 = None
obs = env.reset()
reset = True
# storing stuff in a temporary directory while it's working
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file_1 = os.path.join(td, "model_1")
temp_file_1 = os.path.join(td, "temp_1")
model_saved_1 = False
if multiplayer:
model_file_2 = os.path.join(td, "model_2")
temp_file_2 = os.path.join(td, "temp_2")
model_saved_2 = False
if tf.train.latest_checkpoint(td) is not None:
if multiplayer:
# load both models if multiplayer is on
load_variables(model_file_1, sess_1)
logger.log('Loaded model 1 from {}'.format(model_file_1))
model_saved_1 = True
load_variables(model_file_2, sess_2)
logger.log('Loaded model 2 from {}'.format(model_file_2))
model_saved_2 = True
# otherwise just load the first one
else:
load_variables(model_file_1, sess_1)
logger.log('Loaded model from {}'.format(model_file_1))
model_saved_1 = True
# I have separate load variables for single-player and multiplayer
# this should be None if multiplayer is on
elif load_path is not None:
load_variables(load_path, sess_1)
logger.log('Loaded model from {}'.format(load_path))
# load the separate models in for multiplayer
# should load the variables into the appropriate sessions
# my format may restrict things to working properly only when a Player 1 model is loaded into session 1, and same for Player 2
# however, in practice, the models won't work properly otherwise
elif multiplayer:
if load_path_1 is not None:
load_variables(load_path_1, sess_1)
logger.log('Loaded model 1 from {}'.format(load_path_1))
if load_path_2 is not None:
load_variables(load_path_2, sess_2)
logger.log('Loaded model 2 from {}'.format(load_path_2))
# actual training starts here
for t in range(total_timesteps):
# use this for updating purposes
actual_t = t + 1
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = -np.log(1. - exploration.value(
t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
# receive model 1's action based on the model and observation
action_1 = act_1(np.array(obs)[None],
update_eps=update_eps,
**kwargs)[0]
env_action_1 = action_1
# do the same for model 2 if in multiplayer
if multiplayer:
action_2 = act_2(np.array(obs)[None],
update_eps=update_eps,
**kwargs)[0]
env_action_2 = action_2
reset = False
# apply actions to the environment
if multiplayer:
new_obs, rew_1, rew_2, done, _ = env.step(
env_action_1, env_action_2)
# apply single action if there isn't a second model
else:
new_obs, rew_1, rew_2, done, _ = env.step(env_action_1)
# manual clipping for Space Invaders multiplayer
if isSpaceInvaders and multiplayer:
# don't reward a player when the other player dies
# change the reward to 0
# the only time either player will get rewarded 200 is when the other player dies
if rew_1 >= 200:
rew_1 = rew_1 - 200.0
if rew_2 >= 200:
rew_2 = rew_2 - 200.0
# manually clip the rewards using the sign function
rew_1 = np.sign(rew_1)
rew_2 = np.sign(rew_2)
combo_factor = 0.25
rew_1_combo = rew_1 + combo_factor * rew_2
rew_2_combo = rew_2 + combo_factor * rew_1
rew_1 = rew_1_combo
rew_2 = rew_2_combo
# Store transition in the replay buffers
replay_buffer_1.add(obs, action_1, rew_1, new_obs, float(done))
if multiplayer:
# pass reward_2 to the second player
# this reward will vary based on the game
replay_buffer_2.add(obs, action_2, rew_2, new_obs, float(done))
obs = new_obs
# separate rewards for each model
episode_rewards_1[-1] += rew_1
if multiplayer:
episode_rewards_2[-1] += rew_2
if done:
obs = env.reset()
episode_rewards_1.append(0.0)
if multiplayer:
episode_rewards_2.append(0.0)
reset = True
if actual_t > learning_starts and actual_t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
# sample from the two replay buffers
if prioritized_replay:
experience_1 = replay_buffer_1.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t_1, actions_1, rewards_1, obses_tp1_1, dones_1,
weights_1, batch_idxes_1) = experience_1
# keep all the variables with separate names
if multiplayer:
experience_2 = replay_buffer_2.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t_2, actions_2, rewards_2, obses_tp1_2, dones_2,
weights_2, batch_idxes_2) = experience_2
# do the same if there's no prioritization
else:
obses_t_1, actions_1, rewards_1, obses_tp1_1, dones_1 = replay_buffer_1.sample(
batch_size)
weights_1, batch_idxes_1 = np.ones_like(rewards_1), None
if multiplayer:
obses_t_2, actions_2, rewards_2, obses_tp1_2, dones_2 = replay_buffer_2.sample(
batch_size)
weights_2, batch_idxes_2 = np.ones_like(
rewards_2), None
# actually train the model based on the samples
td_errors_1 = train_1(obses_t_1, actions_1, rewards_1,
obses_tp1_1, dones_1, weights_1)
if multiplayer:
td_errors_2 = train_2(obses_t_2, actions_2, rewards_2,
obses_tp1_2, dones_2, weights_2)
# give new priority weights to the observations
if prioritized_replay:
new_priorities_1 = np.abs(
td_errors_1) + prioritized_replay_eps
replay_buffer_1.update_priorities(batch_idxes_1,
new_priorities_1)
if multiplayer:
new_priorities_2 = np.abs(
td_errors_2) + prioritized_replay_eps
replay_buffer_2.update_priorities(
batch_idxes_2, new_priorities_2)
if actual_t > learning_starts and actual_t % target_network_update_freq == 0:
# Update target networks periodically.
update_target_1()
if multiplayer:
update_target_2()
# this section is for the purposes of logging stuff
# calculate the average reward over the last 100 episodes
mean_100ep_reward_1 = round(np.mean(episode_rewards_1[-101:-1]), 1)
if multiplayer:
mean_100ep_reward_2 = round(
np.mean(episode_rewards_2[-101:-1]), 1)
num_episodes = len(episode_rewards_1)
# every given number of episodes log and print out the appropriate stuff
if done and print_freq is not None and len(
episode_rewards_1) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
# print out both rewards if multiplayer
if multiplayer:
logger.record_tabular("mean 100 episode reward 1",
mean_100ep_reward_1)
logger.record_tabular("mean 100 episode reward 2",
mean_100ep_reward_2)
else:
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward_1)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(t)))
logger.dump_tabular()
# save best-performing version of each model
# I've opted out of this for competitive multiplayer because it's difficult to determine what's "best"
if (checkpoint_freq is not None and actual_t > learning_starts
and num_episodes > 100
and actual_t % checkpoint_freq == 0):
# if there's a best reward, save it as the new best model
if saved_mean_reward_1 is None or mean_100ep_reward_1 > saved_mean_reward_1:
if print_freq is not None:
if multiplayer:
logger.log(
"Saving model 1 due to mean reward increase: {} -> {}"
.format(saved_mean_reward_1,
mean_100ep_reward_1))
else:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward_1,
mean_100ep_reward_1))
save_variables(model_file_1, sess_1)
model_saved_1 = True
saved_mean_reward_1 = mean_100ep_reward_1
if multiplayer and (saved_mean_reward_2 is None or
mean_100ep_reward_2 > saved_mean_reward_2):
if print_freq is not None:
logger.log(
"Saving model 2 due to mean reward increase: {} -> {}"
.format(saved_mean_reward_2, mean_100ep_reward_2))
save_variables(model_file_2, sess_2)
model_saved_2 = True
saved_mean_reward_2 = mean_100ep_reward_2
# restore models at the end to the best performers
if model_saved_1:
if print_freq is not None:
logger.log("Restored model 1 with mean reward: {}".format(
saved_mean_reward_1))
load_variables(model_file_1, sess_1)
if multiplayer and model_saved_2:
if print_freq is not None:
logger.log("Restored model 2 with mean reward: {}".format(
saved_mean_reward_2))
load_variables(model_file_2, sess_2)
return act_1, act_2, sess_1, sess_2
def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs
):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
batch_size: int
size of a batch sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
############################## RL-S Prepare #############################################
# model saved name
saved_name = "0817"
#####
# Setup Training Record
#####
save_new_data = False
create_new_file = False
create_new_file_rule = create_new_file
save_new_data_rule = save_new_data
create_new_file_RL = False
save_new_data_RL = save_new_data
create_new_file_replay_buffer = False
save_new_data_replay_buffer = save_new_data
is_training = False
trajectory_buffer = deque(maxlen=20)
if create_new_file_replay_buffer:
if osp.exists("recorded_replay_buffer.txt"):
os.remove("recorded_replay_buffer.txt")
else:
replay_buffer_dataset = np.loadtxt("recorded_replay_buffer.txt")
for data in replay_buffer_dataset:
obs, action, rew, new_obs, done = _extract_data(data)
replay_buffer.add(obs, action, rew, new_obs, done)
recorded_replay_buffer_outfile = open("recorded_replay_buffer.txt","a")
recorded_replay_buffer_format = " ".join(("%f",)*31)+"\n"
#####
# Setup Rule-based Record
#####
create_new_file_rule = True
# create state database
if create_new_file_rule:
if osp.exists("state_index_rule.dat"):
os.remove("state_index_rule.dat")
os.remove("state_index_rule.idx")
if osp.exists("visited_state_rule.txt"):
os.remove("visited_state_rule.txt")
if osp.exists("visited_value_rule.txt"):
os.remove("visited_value_rule.txt")
visited_state_rule_value = []
visited_state_rule_counter = 0
else:
visited_state_rule_value = np.loadtxt("visited_value_rule.txt")
visited_state_rule_value = visited_state_rule_value.tolist()
visited_state_rule_counter = len(visited_state_rule_value)
visited_state_rule_outfile = open("visited_state_rule.txt", "a")
visited_state_format = " ".join(("%f",)*14)+"\n"
visited_value_rule_outfile = open("visited_value_rule.txt", "a")
visited_value_format = " ".join(("%f",)*2)+"\n"
visited_state_tree_prop = rindex.Property()
visited_state_tree_prop.dimension = 14
visited_state_dist = np.array([[0.2, 2, 10, 0.2, 2, 10, 0.2, 2, 10, 0.2, 2, 10, 0.2, 2]])
visited_state_rule_tree = rindex.Index('state_index_rule',properties=visited_state_tree_prop)
#####
# Setup RL-based Record
#####
if create_new_file_RL:
if osp.exists("state_index_RL.dat"):
os.remove("state_index_RL.dat")
os.remove("state_index_RL.idx")
if osp.exists("visited_state_RL.txt"):
os.remove("visited_state_RL.txt")
if osp.exists("visited_value_RL.txt"):
os.remove("visited_value_RL.txt")
if create_new_file_RL:
visited_state_RL_value = []
visited_state_RL_counter = 0
else:
visited_state_RL_value = np.loadtxt("visited_value_RL.txt")
visited_state_RL_value = visited_state_RL_value.tolist()
visited_state_RL_counter = len(visited_state_RL_value)
visited_state_RL_outfile = open("visited_state_RL.txt", "a")
visited_state_format = " ".join(("%f",)*14)+"\n"
visited_value_RL_outfile = open("visited_value_RL.txt", "a")
visited_value_format = " ".join(("%f",)*2)+"\n"
visited_state_tree_prop = rindex.Property()
visited_state_tree_prop.dimension = 14
visited_state_dist = np.array([[0.2, 2, 10, 0.2, 2, 10, 0.2, 2, 10, 0.2, 2, 10, 0.2, 2]])
visited_state_RL_tree = rindex.Index('state_index_RL',properties=visited_state_tree_prop)
############################## RL-S Prepare End #############################################
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = -np.log(1. - exploration.value(t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action, q_function_cz = act(np.array(obs)[None], update_eps=update_eps, **kwargs)
# RLS_action = generate_RLS_action(obs,q_function_cz,action,visited_state_rule_value,
# visited_state_rule_tree,visited_state_RL_value,
# visited_state_RL_tree,is_training)
RLS_action = 0
env_action = RLS_action
reset = False
new_obs, rew, done, _ = env.step(env_action)
########### Record data in trajectory buffer and local file, but not in replay buffer ###########
trajectory_buffer.append((obs, action, float(rew), new_obs, float(done)))
# Store transition in the replay buffer.
# replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew # safe driving is 1, collision is 0
while len(trajectory_buffer)>10:
# if safe driving for 10(can be changed) steps, the state is regarded as safe
obs_left, action_left, rew_left, new_obs_left, done_left = trajectory_buffer.popleft()
# save this state in local replay buffer file
if save_new_data_replay_buffer:
recorded_data = _wrap_data(obs_left, action_left, rew_left, new_obs_left, done_left)
recorded_replay_buffer_outfile.write(recorded_replay_buffer_format % tuple(recorded_data))
# put this state in replay buffer
replay_buffer.add(obs_left[0], action_left, float(rew_left), new_obs_left[0], float(done_left))
action_to_record = action_left
r_to_record = rew_left
obs_to_record = obs_left
# save this state in rule-based or RL-based visited state
if action_left == 0:
if save_new_data_rule:
visited_state_rule_value.append([action_to_record,r_to_record])
visited_state_rule_tree.insert(visited_state_rule_counter,
tuple((obs_to_record-visited_state_dist).tolist()[0]+(obs_to_record+visited_state_dist).tolist()[0]))
visited_state_rule_outfile.write(visited_state_format % tuple(obs_to_record[0]))
visited_value_rule_outfile.write(visited_value_format % tuple([action_to_record,r_to_record]))
visited_state_rule_counter += 1
else:
if save_new_data_RL:
visited_state_RL_value.append([action_to_record,r_to_record])
visited_state_RL_tree.insert(visited_state_RL_counter,
tuple((obs_to_record-visited_state_dist).tolist()[0]+(obs_to_record+visited_state_dist).tolist()[0]))
visited_state_RL_outfile.write(visited_state_format % tuple(obs_to_record[0]))
visited_value_RL_outfile.write(visited_value_format % tuple([action_to_record,r_to_record]))
visited_state_RL_counter += 1
################# Record data end ########################
if done:
"""
Get collision or out of multilane map
"""
####### Record the trajectory data and add data in replay buffer #########
_, _, rew_right, _, _ = trajectory_buffer[-1]
while len(trajectory_buffer)>0:
obs_left, action_left, rew_left, new_obs_left, done_left = trajectory_buffer.popleft()
action_to_record = action_left
r_to_record = (rew_right-rew_left)*gamma**len(trajectory_buffer) + rew_left
# record in local replay buffer file
if save_new_data_replay_buffer:
obs_to_record = obs_left
recorded_data = _wrap_data(obs_left, action_left, r_to_record, new_obs_left, done_left)
recorded_replay_buffer_outfile.write(recorded_replay_buffer_format % tuple(recorded_data))
# record in replay buffer for trainning
replay_buffer.add(obs_left[0], action_left, float(r_to_record), new_obs_left[0], float(done_left))
# save visited rule/RL state data in local file
if action_left == 0:
if save_new_data_rule:
visited_state_rule_value.append([action_to_record,r_to_record])
visited_state_rule_tree.insert(visited_state_rule_counter,
tuple((obs_to_record-visited_state_dist).tolist()[0]+(obs_to_record+visited_state_dist).tolist()[0]))
visited_state_rule_outfile.write(visited_state_format % tuple(obs_to_record[0]))
visited_value_rule_outfile.write(visited_value_format % tuple([action_to_record,r_to_record]))
visited_state_rule_counter += 1
else:
if save_new_data_RL:
visited_state_RL_value.append([action_to_record,r_to_record])
visited_state_RL_tree.insert(visited_state_RL_counter,
tuple((obs_to_record-visited_state_dist).tolist()[0]+(obs_to_record+visited_state_dist).tolist()[0]))
visited_state_RL_outfile.write(visited_state_format % tuple(obs_to_record[0]))
visited_value_RL_outfile.write(visited_value_format % tuple([action_to_record,r_to_record]))
visited_state_RL_counter += 1
####### Recorded #####
obs = env.reset()
episode_rewards.append(0.0)
reset = True
############### Trainning Part Start #####################
if not is_training:
# don't need to train the model
continue
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
rew_str = str(mean_100ep_reward)
path = osp.expanduser("~/models/carlaok_checkpoint/"+saved_name+"_"+rew_str)
act.save(path)
#### close the file ####
visited_state_rule_outfile.close()
visited_value_rule_outfile.close()
recorded_replay_buffer_outfile.close()
if not is_training:
testing_record_outfile.close()
#### close the file ###
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
load_variables(model_file)
return act
def init_wrapper(env,
network_type,
lr=1e-4,
gamma=1.0,
param_noise=True,
buffer_size=int(1e5),
prioritized_replay_alpha=.6,
prioritized_replay=True,
prioritized_replay_beta_iters=None,
prioritized_replay_beta=.4,
exploration_fraction=.1,
grad_norm_clipping=10,
total_timesteps=int(1e6),
exploration_final_eps=0.02,
**network_kwargs):
# decomposes baseline deepq into initialize and inference components
# basically copied from deepqn repository
# see baselines repo for concise param documentation
q_func = build_q_func(network_type, **network_kwargs)
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=grad_norm_clipping,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
# WARNING: do not use internal replay buffer, use baselines only for
# stability reasons
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
# return hashed objects
return {
'train_function': train,
'act_function': act,
'replay_buffer': replay_buffer,
'update_target_function': update_target,
'exploration_scheme': exploration,
'beta_schedule': beta_schedule
}
def learn(env,
network,
seed=None,
lr=1e-3,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
num_cpu=5,
callback=None,
scope='co_deepq',
pilot_tol=0,
pilot_is_human=False,
reuse=False,
load_path=None,
**network_kwargs):
# Create all the functions necessary to train the model
sess = get_session() #tf.Session(graph=tf.Graph())
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
using_control_sharing = True #pilot_tol > 0
if pilot_is_human:
utils.human_agent_action = init_human_action()
utils.human_agent_active = False
act, train, update_target, debug = co_build_train(
scope=scope,
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
reuse=tf.AUTO_REUSE if reuse else False,
using_control_sharing=using_control_sharing)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size,
alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
episode_outcomes = []
saved_mean_reward = None
obs = env.reset()
reset = True
prev_t = 0
rollouts = []
if not using_control_sharing:
exploration = LinearSchedule(schedule_timesteps=int(
exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
masked_obs = mask_helipad(obs)
act_kwargs = {}
if using_control_sharing:
if pilot_is_human:
act_kwargs['pilot_action'] = env.unwrapped.pilot_policy(
obs[None, :9])
else:
act_kwargs[
'pilot_action'] = env.unwrapped.pilot_policy.step(
obs[None, :9])
act_kwargs['pilot_tol'] = pilot_tol if not pilot_is_human or (
pilot_is_human and utils.human_agent_active) else 0
else:
act_kwargs['update_eps'] = exploration.value(t)
#action = act(masked_obs[None, :], **act_kwargs)[0][0]
action = act(np.array(masked_obs)[None], **act_kwargs)[0][0]
env_action = action
reset = False
new_obs, rew, done, info = env.step(env_action)
if pilot_is_human:
env.render()
# Store transition in the replay buffer.
masked_new_obs = mask_helipad(new_obs)
replay_buffer.add(masked_obs, action, rew, masked_new_obs,
float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if pilot_is_human:
utils.human_agent_action = init_human_action()
utils.human_agent_active = False
time.sleep(2)
if t > learning_starts and t % train_freq == 0:
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
episode_outcomes.append(rew)
episode_rewards.append(0.0)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
mean_100ep_succ = round(
np.mean(
[1 if x == 100 else 0 for x in episode_outcomes[-101:-1]]),
2)
mean_100ep_crash = round(
np.mean([
1 if x == -100 else 0 for x in episode_outcomes[-101:-1]
]), 2)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(
episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward",
mean_100ep_reward)
logger.record_tabular("mean 100 episode succ", mean_100ep_succ)
logger.record_tabular("mean 100 episode crash",
mean_100ep_crash)
logger.dump_tabular()
if checkpoint_freq is not None and t > learning_starts and num_episodes > 100 and t % checkpoint_freq == 0 and (
saved_mean_reward is None
or mean_100ep_reward > saved_mean_reward):
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}".
format(saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
reward_data = {'rewards': episode_rewards, 'outcomes': episode_outcomes}
return act, reward_data
