def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
self.critic_target.model.set_weights(self.critic_local.model.get_weights())
self.actor_target.model.set_weights(self.actor_local.model.get_weights())
self.exploration_mu = 0
self.exploration_theta = 0.10 # same direction
self.exploration_sigma = 0.001 # random noise
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
self.gamma = 0.90 # discount factor
self.tau = 0.1 # for soft update of target parameters
self.best_score = -np.inf
self.score = 0
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
self.gamma = 0.99
self.tau = 0.001
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(self.critic_local.model.get_weights())
self.actor_target.model.set_weights(self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.01
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor 0.99
self.tau = 0.1 # for soft update of target parameters 0.01
def __init__(self, task, seed=None, render=False):
self.env = task.env
self.total_reward = 0
self.steps = 0
self.action_repeat = 3
self.render = render
# Score tracker and learning parameters
self.score = -np.inf
self.best_w = None
self.best_score = -np.inf
self.noise_scale = 0.1
#counter
self.count = 0
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(1, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
def __init__(self,
task,
buffer_size=100000,
batch_size=64,
gamma=0.99,
tau=0.01):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
learning_rate=1e-3)
self.actor_target = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
learning_rate=1e-3)
# Critic (Value) Model
self.critic_local = Critic(self.state_size,
self.action_size,
learning_rate=1e-4)
self.critic_target = Critic(self.state_size,
self.action_size,
learning_rate=1e-4)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
# Noise process
self.noise = OUNoise(size=self.action_size)
# Replay memory
self.buffer_size = buffer_size
self.batch_size = batch_size # 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = gamma # 0.99 discount factor
self.tau = tau # 0.01 for soft update of target parameters
# 初始化
self.last_state = None
self.total_reward = 0.0
# Score tracker and learning parameters
self.score = 0
self.best_score = -np.inf
self.count = 0
def __init__(self, obs_space, act_space, sess, n_agents, name):
self.obs_space = obs_space
self.act_space = act_space
self.n_agents = n_agents
self.dqn = DQN(sess, obs_space, sup_len, act_space, n_agents, name)
self.rb = ReplayBuffer(capacity=rb_capacity)
self.train_cnt = 0
def __init__(self, task, sess, stats):
self.sess = sess
self.task = task
self.stats = stats
tau = 0.01
learning_rate = 2e-4
self.critic_local = QNetwork(sess,
task,
stats,
name='critic_local',
hidden_units=64,
dropout_rate=0.2)
self.critic_target = QNetwork(sess,
task,
stats,
name='critic_target',
hidden_units=64,
dropout_rate=0.2)
self.actor_local = Policy(sess,
task,
stats,
name='actor_local',
hidden_units=32,
dropout_rate=0.2)
self.actor_target = Policy(sess,
task,
stats,
name='actor_target',
hidden_units=32,
dropout_rate=0.2)
soft_copy_critic_ops = self._create_soft_copy_op('critic_local',
'critic_target',
tau=tau)
soft_copy_actor_ops = self._create_soft_copy_op('actor_local',
'actor_target',
tau=tau)
self._soft_copy_ops = []
self._soft_copy_ops.extend(soft_copy_critic_ops)
self._soft_copy_ops.extend(soft_copy_actor_ops)
self.gamma = 0.99 # reward discount rate
# Exploration noise process
exploration_mu = 0
exploration_theta = 0.15
exploration_sigma = 0.15
self.noise = OUNoise(task.action_size, exploration_mu,
exploration_theta, exploration_sigma)
# Replay memory
self.batch_size = 256
self.memory = ReplayBuffer(buffer_size=10000, decay_steps=1000)
self.sess.run(tf.global_variables_initializer())
def __init__(self, obs_space, act_space, sess, n_agents, name):
self.act_space = act_space
self.n_agents = n_agents
self.ped_dqn = DQN(sess, obs_space, sup_len, act_space, n_agents, name)
self.action_rb = ReplayBuffer(capacity=rb_capacity)
self.train_cnt = 0
self.sns_q = None
def __init__(self, task, params={}):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
params=params)
self.actor_target = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
params=params)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size, params)
self.critic_target = Critic(self.state_size, self.action_size, params)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15 # same direction
self.exploration_sigma = 0.001 # random noise
if (params.get("sigma")):
self.exploration_sigma = params.get("sigma")
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
if (params.get("batch_size")):
self.batch_size = params.get("batch_size")
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.1 # for soft update of target parameters
#self.gamma = 0.9
#self.tau = 0.05
# Statistics
self.best_score = -np.inf
self.score = 0
class Agent(object):
def __init__(self, obs_space, act_space, sess, n_agents, name):
self.act_space = act_space
self.n_agents = n_agents
self.dqn = DQN(sess, obs_space, sup_len, act_space, n_agents, name)
self.rb = ReplayBuffer(capacity=rb_capacity)
self.train_cnt = 0
self.sns_q = None
def act_multi(self, obs, random):
q_values = self.dqn.get_q_values([obs])[0]
r_action = np.random.randint(self.act_space, size=(len(obs)))
action_n = (
(random + 1) % 2) * (q_values.argmax(axis=1)) + (random) * r_action
return action_n
def incentivize_multi(self, info):
state, action, reward, next_state, done = info
return reward
def add_to_memory(self, exp):
self.rb.add_to_memory(exp)
def sync_target(self):
self.dqn.training_target_qnet()
def train(self, use_rx):
data = self.rb.sample_from_memory(minibatch_size)
state = np.asarray([x[0] for x in data])
action = np.asarray([x[1] for x in data])
base_reward = np.asarray([x[2] for x in data])
next_state = np.asarray([x[3] for x in data])
done = np.asarray([x[4] for x in data])
not_done = (done + 1) % 2
if use_rx:
rx_inc = np.asarray([x[5] for x in data])
reward = base_reward + rx_inc
else:
reward = base_reward
td_error, _ = self.dqn.training_qnet(state, action, reward, not_done,
next_state)
self.train_cnt += 1
if self.train_cnt % (target_update) == 0:
self.dqn.training_target_qnet()
return td_error
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# AE: Although OUNoise gives me a convenient set of randomness for each of the rotors, I still need
# AE: to make a decision myself on how to apply the randomness and how to manage its magnitude
# AE: (i.e. my eplore vs exploit strategy). These variables will do that.
self.explore_start = 1.0 # AE: exploration probability at start
self.explore_stop = 0.001 # AE: minimum exploration probability
self.decay_rate = 0.003 # AE: exponential decay rate for exploration prob
self.magnitude_coeff = 0.1 # AE: a coefficient to limit randomness
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0 # AE: additive to the noise. mu * theta will be directly added
self.exploration_theta = 0.15 # AE: old noise will be multiplied by this
self.exploration_sigma = 0.2 # AE: new noise will be multiplied by this
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
# AE: The learning rate. How much we trust the new values compared to the old ones.
self.tau = 0.0001 # for soft update of target parameters
# AE: current reward in learning procedure (for statistics)
self.score = -np.inf
# Episode variables
self.reset_episode()
class Agent(object):
def __init__(self, global_state_space, obs_space, act_space, n_agents,
sess, name):
self.act_space = act_space
self.obs_space = obs_space
self.n_agents = n_agents
self.dqn = DQN(sess, global_state_space, obs_space, act_space,
n_agents, name)
self.rb = ReplayBuffer(capacity=rb_capacity)
self.train_cnt = 0
def act_multi(self, obs, random):
if random.all():
return np.random.randint(self.act_space, size=(len(obs)))
q_values = self.dqn.get_q_values([obs])[0]
r_action = np.random.randint(self.act_space, size=(len(obs)))
action_n = (
(random + 1) % 2) * (q_values.argmax(axis=1)) + (random) * r_action
return action_n
def add_to_memory(self, exp):
self.rb.add_to_memory(exp)
def sync_target(self):
self.dqn.training_target_qnet()
def train(self):
data = self.rb.sample_from_memory(minibatch_size)
state = np.asarray([x[0] for x in data])
action = np.asarray([x[1] for x in data])
reward = np.asarray([x[2] for x in data])
next_state = np.asarray([x[3] for x in data])
done = np.asarray([x[4] for x in data])
global_state = np.asarray([x[5] for x in data])
next_global_state = np.asarray([x[6] for x in data])
not_done = (done + 1) % 2
td_error, _ = self.dqn.training_qnet(global_state, state, action,
reward, not_done,
next_global_state, next_state)
self.train_cnt += 1
if self.train_cnt % target_update == 0:
self.dqn.training_target_qnet()
return td_error
def __init__(self,
env,
actor_model,
critic_model,
gamma=0.99,
tau=1e-3,
critic_lr=1e-3,
actor_lr=1e-4,
critic_decay=0.):
# Changed this to use generic env instead of Task
super().__init__(env)
self.state_size = env.observation_space.shape[0]
self.action_size = env.action_space.shape[0]
self.action_low = env.action_space.low
self.action_high = env.action_space.high
# Algorithm parameters
self.gamma = gamma # discount factor
self.tau = tau # for soft update of target parameters
self.critic_lr = critic_lr
self.actor_lr = actor_lr
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high,
self.actor_lr)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high,
self.actor_lr)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size,
self.critic_lr)
self.critic_target = Critic(self.state_size, self.action_size,
self.critic_lr)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
def __init__(self, task, seed=None, render=False):
self.env = task.env
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
self.total_reward = 0
self.steps = 0
self.action_repeat = 3
self.render = render
# Score tracker and learning parameters
self.score = -np.inf
self.best_w = None
self.best_score = -np.inf
self.noise_scale = 0.1
#counter
self.count = 0
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(1, self.exploration_mu, self.exploration_theta,
self.exploration_sigma)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.01 # for soft update of target parameters
def __init__(self,
actor_model,
tgt_actor_model,
critic_model,
tgt_critic_model,
action_limits,
actor_lr=1e-4,
critic_lr=1e-3,
critic_decay=1e-2,
tau=1e-3,
gamma=0.99,
process=None,
rb_size=1e6,
minibatch_size=64,
warmup_episodes=0,
episodes_trained=0,
train_scores=None,
test_scores=None,
best_train_score=-np.inf):
# Changed this to use generic env instead of Task
super().__init__(warmup_episodes, episodes_trained, train_scores,
test_scores, best_train_score)
self.actor = Actor(actor_model, critic_model, lr=actor_lr)
self.tgt_actor = Actor(tgt_actor_model, tgt_critic_model, lr=actor_lr)
self.tgt_actor.set_weights(self.actor.get_weights())
self.critic = Critic(critic_model, lr=critic_lr, decay=critic_decay)
self.tgt_critic = Critic(tgt_critic_model,
lr=critic_lr,
decay=critic_decay)
self.tgt_critic.set_weights(self.critic.get_weights())
self.action_limits = action_limits
self.process = process
self.minibatch_size = minibatch_size
self.buffer = ReplayBuffer(int(rb_size), self.minibatch_size)
self.tau = tau
self.gamma = gamma
self.state_space = K.int_shape(critic_model.inputs[0])[1]
self.action_space = K.int_shape(critic_model.inputs[1])[1]
self.learning_phase = 1
if process is None:
self.process = OUNoise(size=self.action_space,
theta=0.15,
mu=0,
sigma=0.2)
else:
self.process = process
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(self.critic_local.model.get_weights())
self.actor_target.model.set_weights(self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.01 # for soft update of target parameters
# from plicy search
self.action_range = self.action_high - self.action_low
self.w = np.random.normal(
size=(self.state_size, self.action_size), # weights for simple linear policy: state_space x action_space
scale=(self.action_range / (2 * self.state_size))) # start producing actions in a decent range
# Score tracker and learning parameters
self.score = -np.inf
self.best_w = None
self.best_score = -np.inf
self.noise_scale = 0.1
#counter
self.count = 0
def __init__(self, task, verbose=False):
self.verbose = verbose
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
#log_path = '/tmp/logs'
#self.callback = callbacks.TensorBoard(log_dir=log_path, histogram_freq=1,
# write_images=False, write_grads=True, write_graph=False)
#self.callback.set_model(self.critic_local.model)
#log_path = '/tmp/logs'
#self.writer = tf.summary.FileWriter(log_path)
#self.learn_counter = 0
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0.1
self.exploration_theta = 0.2
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 512
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.015 # for soft update of target parameters
def __init__(self, env_reset, state_size, action_size, action_low, action_high):
"""Params:
env_reset: callback function to reset environemnt at end of episode
state_size: dimension of state space
action_size: dimension of action space
action_low: float - minimum action value
action_high: float - maximum action value
"""
self.training_steps = 0 # number of training steps run so far
self.env_reset = env_reset
self.state_size = state_size
self.action_size = action_size
self.action_low = action_low
self.action_high = action_high
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 1e-3 # for soft update of target parameters
self.critic_decay = 1e-2 # L2 weight decay for critic (regularization)
self.critic_lr = 1e-3 # Learning rate for critic
self.critic_alpha = 1e-2 # Leaky ReLU alpha for critic
self.actor_lr = 1e-4 # Learning rate for actor
self.actor_alpha = 1e-2 # Leaky ReLU alpha for actor
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high, self.actor_lr, self.actor_alpha)
self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high, self.actor_lr, self.actor_alpha)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size, self.critic_lr, self.critic_decay, self.critic_alpha)
self.critic_target = Critic(self.state_size, self.action_size, self.critic_lr, self.critic_decay,self.critic_alpha)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(self.critic_local.model.get_weights())
self.actor_target.model.set_weights(self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = int(1e6)
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
def __init__(self, task):
"""Initialize DDPG Agent instance."""
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_high = task.action_high
self.action_low = task.action_low
# Initializing local and target Actor Models
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_high, self.action_low)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_high, self.action_low)
# Initializing local and target Critic Models
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay Memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.01 # for soft update of target parameters
# Additional Parameters
self.best_score = -np.inf
self.total_reward = 0.0
self.count = 0
self.score = 0
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
# Y.W. changing sigma
self.exploration_sigma = 0.3 #0.3 #0.2 # 0.3
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
# Y.W. extending buffer_size
self.buffer_size = 1000000 #100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
# self.tau = 0.01 # for soft update of target parameters
# Y.W.
self.tau = 0.001 # 0.001
# simple reword cash
self.total_reward = 0.0
self.best_total_reward = -np.inf
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.01 # for soft update of target parameters
# ======================== Custom amednments : Score tracker and learning parameters ========================
self.best_score = -np.inf
self.score = 0
self.total_reward = 0.0
self.count = 0
self.best_position = np.zeros(3)
def load_model(cls, filename):
with open(filename + '.ddpg_agent') as f:
m = pickle.load(f)
m.actor_local = load_model(filename + '.actor_local')
m.actor_target = load_model(filename + '.actor_target')
m.critic_local = load_model(filename + '.critic_local')
m.critic_target = load_model(filename + '.critic_target')
m.replay_buffer = ReplayBuffer(m.buffer_size, m.batch_size)
return m
def set_params(self,
mu=0.1,
sigma=0.1,
theta=0.1,
buffer_size=1e+8,
batch_size=128,
gamma=0.99,
tau=1e-3):
self.exploration_mu = mu
self.exploration_sigma = sigma
self.exploration_theta = theta
self.noise = noise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
self.buffer_size = int(buffer_size)
self.batch_size = int(batch_size)
self.buffer = ReplayBuffer(self.buffer_size)
self.gamma = gamma
self.tau = tau
def setup_replay_buffer_(self):
"""
Setup a replay buffer.
:return: None.
"""
if self.prioritized_replay:
self.replay_buffer = PrioritizedReplayBuffer(self.buffer_size, alpha=self.pr_alpha)
self.beta_schedule = LinearSchedule(self.max_timesteps, initial_p=self.pr_beta, final_p=self.final_explore)
else:
self.replay_buffer = ReplayBuffer(self.buffer_size)
self.beta_schedule = None
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.001
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size)
self.gamma = 0.99
self.tau = 0.1
self.learning_rate = 0.0005
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
self.actor_local = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
learning_rate=self.learning_rate)
self.actor_target = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
learning_rate=self.learning_rate)
self.critic_local = Critic(self.state_size,
self.action_size,
learning_rate=self.learning_rate)
self.critic_target = Critic(self.state_size,
self.action_size,
learning_rate=self.learning_rate)
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
#actor model
self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
#Critic model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
#Initialize target model params with local params
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
#Initialize noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
#Replay memory Initialization
self.buffer_size, self.batch_size = 2000000, 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
#Initialize algorithm parameters
self.gamma, self.tau = 0.95, 0.001
#Initialize scores
self.score, self.best_score = -np.inf, -np.inf
def __init__(self, task):
"""Initialize DDPG Agent instance."""
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_high = task.action_high
self.action_low = task.action_low
# Initializing local and target Actor Models
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_high, self.action_low)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_high, self.action_low)
# Initializing local and target Critic Models
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.actor_target.model.set_weights(self.actor_local.model.get_weights())
self.critic_target.model.set_weights(self.critic_local.model.get_weights())
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay Memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.01 # for soft update of target parameters
# Additional Parameters
self.best_score = -np.inf
self.total_reward = 0.0
self.count = 0
self.score = 0
def __init__(self, action_size):
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15 # same direction
self.exploration_sigma = 0.001 # random noise
self.noise = OUNoise(action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
#self.memory = Memory(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.1 # for soft update of target parameters
class Agent():
"""Reinforcement Learning agent that learns using DDPG."""
def __init__(self, task):
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(self.critic_local.model.get_weights())
self.actor_target.model.set_weights(self.actor_local.model.get_weights())
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15 # same direction
self.exploration_sigma = 0.001 # random noise
#self.exploration_mu = 0
#self.exploration_theta = 0.15
#self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.1 # for soft update of target parameters
# Compute the ongoing top score
self.top_score = -np.inf
self.score = 0
def reset_episode(self):
self.noise.reset()
state = self.task.reset()
self.last_state = state
self.score = 0
return state
def step(self, action, reward, next_state, done):
# Save experience / reward
self.memory.add(self.last_state, action, reward, next_state, done)
# Learn, if enough samples are available in memory
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences)
# Roll over last state and action
self.last_state = next_state
# stats
self.score += reward
if done:
if self.score > self.top_score:
self.top_score = self.score
def act(self, states):
"""Returns actions for given state(s) as per current policy."""
state = np.reshape(states, [-1, self.state_size])
action = self.actor_local.model.predict(state)[0]
return list(action + self.noise.sample()) # add some noise for exploration
def learn(self, experiences):
"""Update policy and value parameters using given batch of experience tuples."""
# Convert experience tuples to separate arrays for each element (states, actions, rewards, etc.)
states = np.vstack([e.state for e in experiences if e is not None])
actions = np.array([e.action for e in experiences if e is not None]).astype(np.float32).reshape(-1, self.action_size)
rewards = np.array([e.reward for e in experiences if e is not None]).astype(np.float32).reshape(-1, 1)
dones = np.array([e.done for e in experiences if e is not None]).astype(np.uint8).reshape(-1, 1)
next_states = np.vstack([e.next_state for e in experiences if e is not None])
# Get predicted next-state actions and Q values from target models
# Q_targets_next = critic_target(next_state, actor_target(next_state))
actions_next = self.actor_target.model.predict_on_batch(next_states)
Q_targets_next = self.critic_target.model.predict_on_batch([next_states, actions_next])
# Compute Q targets for current states and train critic model (local)
Q_targets = rewards + self.gamma * Q_targets_next * (1 - dones)
self.critic_local.model.train_on_batch(x=[states, actions], y=Q_targets)
# Train actor model (local)
action_gradients = np.reshape(self.critic_local.get_action_gradients([states, actions, 0]), (-1, self.action_size))
self.actor_local.train_fn([states, action_gradients, 1]) # custom training function
# Soft-update target models
self.soft_update(self.critic_local.model, self.critic_target.model)
self.soft_update(self.actor_local.model, self.actor_target.model)
def soft_update(self, local_model, target_model):
"""Soft update model parameters."""
local_weights = np.array(local_model.get_weights())
target_weights = np.array(target_model.get_weights())
assert len(local_weights) == len(target_weights), "Local and target model parameters must have the same size"
new_weights = self.tau * local_weights + (1 - self.tau) * target_weights
target_model.set_weights(new_weights)
class DDPG_Agent:
"""Reinforcement learning agent that learns through DDPG."""
def __init__(self, task):
"""Initialize DDPG Agent instance."""
self.task = task
self.state_size = task.state_size
self.action_size = task.action_size
self.action_high = task.action_high
self.action_low = task.action_low
# Initializing local and target Actor Models
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_high, self.action_low)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_high, self.action_low)
# Initializing local and target Critic Models
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.actor_target.model.set_weights(self.actor_local.model.get_weights())
self.critic_target.model.set_weights(self.critic_local.model.get_weights())
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay Memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.01 # for soft update of target parameters
# Additional Parameters
self.best_score = -np.inf
self.total_reward = 0.0
self.count = 0
self.score = 0
def reset_episode(self):
"""Reset episode to initial state."""
self.total_reward = 0.0
self.count = 0
self.noise.reset()
state = self.task.reset()
self.last_state = state
return state
def step(self, action, reward, next_state, done):
"""Take a step."""
self.total_reward += reward
self.count += 1
# Save experience/reward
self.memory.memorize(self.last_state, action, reward, next_state, done)
# Learn if enough samples are available in memory.
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences)
def act(self, state):
"""Returns actions for state(s) according to given policy."""
state = np.reshape(state, [-1, self.state_size])
action = self.actor_local.model.predict(state)[0]
# Add some noise to action for exploration and return
return list(action + self.noise.sample())
def learn(self, experiences):
"""Update policy and value parameters using given batch of experience
tuples."""
self.score = self.total_reward / \
float(self.count) if self.count else 0.0
if self.score > self.best_score:
self.best_score = self.score
states = np.vstack([e.state for e in experiences if e is not None])
actions = np.vstack(
[e.action for e in experiences if e is not None]).astype(
np.float32).reshape(-1, self.action_size)
rewards = np.vstack(
[e.reward for e in experiences if e is not None]).astype(
np.float32).reshape(-1, 1)
dones = np.vstack(
[e.done for e in experiences if e is not None]).astype(
np.uint8).reshape(-1, 1)
next_states = np.vstack(
[e.next_state for e in experiences if e is not None])
# Get predicted next-state actions and Q values from target models
# Q_targets_next = critic_target(next_state, actor_target(next_state))
next_actions = self.actor_target.model.predict_on_batch(next_states)
Q_targets_next = self.critic_target.model.predict_on_batch(
[next_states, next_actions])
# Compute Q targets for current states and train critic model (local)
Q_targets = rewards + self.gamma * Q_targets_next * (1 - dones)
self.critic_local.model.train_on_batch(
x=[states, actions], y=Q_targets)
# Train actor model (local)
# [states, actions, 0] 0 is for No learning Phase
action_gradients = np.reshape(self.critic_local.get_action_gradients(
[states, actions, 0]), (-1, self.action_size))
self.actor_local.train_fn([states, action_gradients, 1])
# Soft-update target models
self.soft_update(self.critic_local.model, self.critic_target.model)
self.soft_update(self.actor_local.model, self.actor_target.model)
def soft_update(self, local_model, target_model):
"""Soft update model parameters."""
local_weights = np.array(local_model.get_weights())
target_weights = np.array(target_model.get_weights())
assert len(local_weights) == len(
target_weights), "Local and target model parameters must \
have the same size"
new_weights = self.tau*local_weights + (1-self.tau)*target_weights
target_model.set_weights(new_weights)
class DDPG_Land():
def __init__(self, task, seed=None, render=False):
self.env = task.env
self.state_size = task.state_size
self.action_size = task.action_size
self.action_low = task.action_low
self.action_high = task.action_high
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# Initialize target model parameters with local model parameters
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
self.total_reward = 0
self.steps = 0
self.action_repeat = 3
self.render = render
# Score tracker and learning parameters
self.score = -np.inf
self.best_w = None
self.best_score = -np.inf
self.noise_scale = 0.1
#counter
self.count = 0
# Replay memory
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Noise process
self.exploration_mu = 0
self.exploration_theta = 0.15
self.exploration_sigma = 0.2
self.noise = OUNoise(1, self.exploration_mu, self.exploration_theta,
self.exploration_sigma)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.01 # for soft update of target parameters
def act(self, s):
# # print('act')
# # a = lunder.heuristic(self.env, s)
# # 1. Testing.
# # 2. Demonstration rollout.
# angle_targ = s[0]*0.5 + s[2]*1.0 # angle should point towards center (s[0] is horizontal coordinate, s[2] hor speed)
# if angle_targ > 0.4: angle_targ = 0.4 # more than 0.4 radians (22 degrees) is bad
# if angle_targ < -0.4: angle_targ = -0.4
# hover_targ = 0.55*np.abs(s[0]) # target y should be proporional to horizontal offset
# # PID controller: s[4] angle, s[5] angularSpeed
# angle_todo = (angle_targ - s[4])*0.5 - (s[5])*1.0
# #print("angle_targ=%0.2f, angle_todo=%0.2f" % (angle_targ, angle_todo))
# # PID controller: s[1] vertical coordinate s[3] vertical speed
# hover_todo = (hover_targ - s[1])*0.5 - (s[3])*0.5
# #print("hover_targ=%0.2f, hover_todo=%0.2f" % (hover_targ, hover_todo))
# if s[6] or s[7]: # legs have contact
# angle_todo = 0
# hover_todo = -(s[3])*0.5 # override to reduce fall speed, that's all we need after contact
# if self.env.continuous:
# a = np.array( [hover_todo*20 - 1, -angle_todo*20] )
# a = np.clip(a, -1, +1)
# else:
# a = 0
# if hover_todo > np.abs(angle_todo) and hover_todo > 0.05: a = 2
# elif angle_todo < -0.05: a = 3
# elif angle_todo > +0.05: a = 1
# # return a
# state = s
"""Returns actions for given state(s) as per current policy."""
state = np.reshape(s, [-1, 24])
action = self.actor_local.model.predict(state)[0]
return list(action + self.noise.sample())
def step(self, action, reward, next_state, done):
# print ("step")
# ob, reward, done, info = self.env.step(action)
# print(ob)
# next_state = ob
# Save experience / reward
reward = np.clip(reward, a_min=-100, a_max=100)
self.memory.add(self.last_state, action, reward, next_state, done)
self.count += 1
self.total_reward += reward
# Learn, if enough samples are available in memory
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences)
# Roll over last state and action
self.last_state = next_state
#from the tutorial SRC
self.score += reward
if done:
# self.score = np.clip(self.score,a_min=-100,a_max=100)
if self.score > self.best_score:
self.best_score = self.score
def soft_update(self, local_model, target_model):
"""Soft update model parameters."""
local_weights = np.array(local_model.get_weights())
target_weights = np.array(target_model.get_weights())
assert len(local_weights) == len(
target_weights
), "Local and target model parameters must have the same size"
new_weights = self.tau * local_weights + (1 -
self.tau) * target_weights
target_model.set_weights(new_weights)
# #from the tutorial SRC
# self.score += reward
# if done:
# if self.score > self.best_score:
# self.best_score = self.score
# # return ob, reward, done
def learn(self, experiences):
"""Update policy and value parameters using given batch of experience tuples."""
# Convert experience tuples to separate arrays for each element (states, actions, rewards, etc.)
states = np.vstack([e.state for e in experiences if e is not None])
actions = np.array([e.action for e in experiences
if e is not None]).astype(np.float32).reshape(
-1, self.action_size)
rewards = np.array([e.reward for e in experiences if e is not None
]).astype(np.float32).reshape(-1, 1)
dones = np.array([e.done for e in experiences
if e is not None]).astype(np.uint8).reshape(-1, 1)
next_states = np.vstack(
[e.next_state for e in experiences if e is not None])
# Get predicted next-state actions and Q values from target models
# Q_targets_next = critic_target(next_state, actor_target(next_state))
actions_next = self.actor_target.model.predict_on_batch(next_states)
Q_targets_next = self.critic_target.model.predict_on_batch(
[next_states, actions_next])
# Compute Q targets for current states and train critic model (local)
Q_targets = rewards + self.gamma * Q_targets_next * (1 - dones)
self.critic_local.model.train_on_batch(x=[states, actions],
y=Q_targets)
# Train actor model (local)
action_gradients = np.reshape(
self.critic_local.get_action_gradients([states, actions, 0]),
(-1, self.action_size))
self.actor_local.train_fn([states, action_gradients,
1]) # custom training function
# Soft-update target models
self.soft_update(self.critic_local.model, self.critic_target.model)
self.soft_update(self.actor_local.model, self.actor_target.model)
# # from policy search
# # Learn by random policy search, using a reward-based score
# # self.score = self.total_reward / float(self.count) if self.count else 0.0
# # if self.score > self.best_score:
# # self.best_score = self.score
# # self.best_w = self.w
# # self.noise_scale = max(0.5 * self.noise_scale, 0.01)
# # else:
# # self.w = self.best_w
# # self.noise_scale = min(2.0 * self.noise_scale, 3.2)
# # self.w = self.w + self.noise_scale * np.random.normal(size=self.w.shape) # equal noise in all directions
def reset(self):
self.steps = 0
self.total_reward = 0
self.count = 0
self.score = 0
# self.best_score = 0
"""Reset the sim to start a new episode."""
ob = self.env.reset()
state = np.concatenate([ob] * self.action_repeat)
self.last_state = state
return state
class Agent():
def __init__(self, task, sess, stats):
self.sess = sess
self.task = task
self.stats = stats
tau = 0.01
learning_rate = 2e-4
self.critic_local = QNetwork(sess,
task,
stats,
name='critic_local',
hidden_units=64,
dropout_rate=0.2)
self.critic_target = QNetwork(sess,
task,
stats,
name='critic_target',
hidden_units=64,
dropout_rate=0.2)
self.actor_local = Policy(sess,
task,
stats,
name='actor_local',
hidden_units=32,
dropout_rate=0.2)
self.actor_target = Policy(sess,
task,
stats,
name='actor_target',
hidden_units=32,
dropout_rate=0.2)
soft_copy_critic_ops = self._create_soft_copy_op('critic_local',
'critic_target',
tau=tau)
soft_copy_actor_ops = self._create_soft_copy_op('actor_local',
'actor_target',
tau=tau)
self._soft_copy_ops = []
self._soft_copy_ops.extend(soft_copy_critic_ops)
self._soft_copy_ops.extend(soft_copy_actor_ops)
self.gamma = 0.99 # reward discount rate
# Exploration noise process
exploration_mu = 0
exploration_theta = 0.15
exploration_sigma = 0.15
self.noise = OUNoise(task.action_size, exploration_mu,
exploration_theta, exploration_sigma)
# Replay memory
self.batch_size = 256
self.memory = ReplayBuffer(buffer_size=10000, decay_steps=1000)
self.sess.run(tf.global_variables_initializer())
def reset_episode(self):
self.noise.reset()
state = self.task.reset()
self.last_state = state
self.memory.decay_a()
return state
def step(self, action, reward, next_state, done):
# Save experience
self._save_experience(self.last_state, action, reward, next_state,
done)
# Learn, if enough samples are available in memory
self.learn()
# Roll over last state and action
self.last_state = next_state
def act(self, state, explore=False):
"""Returns actions for given state(s) as per current policy."""
actor = self.actor_local if explore else self.actor_target
action = actor.act([state], explore)[0]
assert not np.any(np.isnan(action))
if explore:
action = action + self.noise.sample()
action = np.maximum(action, self.task.action_low)
action = np.minimum(action, self.task.action_high)
assert not np.any(np.isnan(action))
assert np.all(action >= self.task.action_low
), "expected less than {:7.3f}, but was {}".format(
task.action_low, action)
assert np.all(action <= self.task.action_high)
return action
def learn(self):
"""Update policy and value parameters using given batch of experience tuples."""
if len(self.memory) < self.batch_size:
return
# Convert experience tuples to separate arrays for each element (states, actions, rewards, etc.)
experiences, experience_indexes = self.memory.sample(self.batch_size)
action_size = self.task.action_size
states = np.vstack([e.state for e in experiences])
actions = np.array([e.action for e in experiences
]).astype(np.float32).reshape(-1, action_size)
rewards = np.array([e.reward for e in experiences
]).astype(np.float32).reshape(-1, 1)
dones = np.array([e.done for e in experiences
]).astype(np.uint8).reshape(-1, 1)
next_states = np.vstack([e.next_state for e in experiences])
# Get predicted next-state actions, Q and V values
actions_next = self.actor_target.act(next_states)
Q_targets_next, V_targets_next = self.critic_target.get_q_and_v(
next_states, actions_next)
# Compute Q targets for current states and train critic model (local)
Q_targets = rewards + self.gamma * Q_targets_next * (1 - dones)
V_targets = rewards + self.gamma * V_targets_next * (1 - dones)
td_errs = self.critic_local.learn(states, actions, Q_targets,
V_targets)
self.memory.update_td_err(experience_indexes, td_errs)
self.memory.scrape_stats(self.stats)
# Train actor model
actions = self.actor_target.act(states)
action_gradients = self.critic_target.get_action_gradients(
states, actions)
self.actor_local.learn(states, action_gradients)
self._soft_copy()
def _save_experience(self, state, action, reward, next_state, done):
"""Adds experience into ReplayBuffer. As a side effect, also learns q network on this sample."""
# Get predicted next-state actions and Q values
actions_next = self.actor_local.act([next_state])
Q_targets_next, _ = self.critic_local.get_q_and_v([next_state],
actions_next)
Q_target_next = Q_targets_next[0]
Q_target = reward + self.gamma * Q_target_next * (1 - done)
td_err = self.critic_local.get_td_err([state], [action], [Q_target])
self.memory.add(Experience(state, action, reward, next_state, done),
td_err)
def _soft_copy(self):
self.sess.run(self._soft_copy_ops)
def _create_soft_copy_op(self, scope_src, scope_dst, tau=0.01):
var_src = tf.trainable_variables(scope=scope_src)
var_dst = tf.trainable_variables(scope=scope_dst)
copy_ops = []
for src, dst in zip(var_src, var_dst):
mixed = tau * src + (1.0 - tau) * dst
copy_op = tf.assign(dst, mixed)
copy_ops.append(copy_op)
return copy_ops
