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 = 0.3
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
self.buffer_size = 1000000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
self.gamma = 0.99
self.tau = 0.001
def __init__(self, cfg):
# Environment configuration
self.action_shape = cfg['env']['action_shape']
# Replay memory
cfg['agent']['memory']['action_shape'] = self.action_shape
self.memory = ReplayBuffer(**cfg['agent']['memory'])
# Algorithm parameters
self.exploration_mu, self.exploration_sigma = cfg['agent']['noise']
self.gamma = cfg['agent']['gamma']
self.tau = cfg['agent']['tau']
state_flatten_shape = [np.prod(self.memory.flatten_state_shape)]
# Actor Model
self.actor = Actor(state_flatten_shape, self.action_shape,
cfg['env']['action_range'], self.tau,
self.memory.batch_size, cfg['actor'])
# Critic Model
self.critic = Critic(state_flatten_shape, self.action_shape, self.tau,
cfg['critic'])
# Flag & Counter
self.training = True
self.episode = 0
self.max_episode_explore = cfg['agent']['explore']
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 = 0.2
self.exploration_sigma = 0.33
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 128
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.9 # discount factor
self.tau = 0.0015 # for soft update of target parameters
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, gamma=0.99 , tau=0.01, buffer_size=100000, batch_size=64 ):
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 # mean/equilibrium value
self.exploration_theta = 0.15 #0.6 #0.15 # how fast the variable returns towards the mean
self.exploration_sigma = 0.2 #0.3 #0.2 # volatility of process
self.noise = OUNoise(self.action_size, self.exploration_mu, self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = buffer_size
self.batch_size = batch_size
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
#Algorithm parameters
self.gamma = gamma
self.tau = tau
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
# Create the actor instances for local and target
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)
# Create the critic instances for local and target
self.critic_local = Critic(self.state_size, self.action_size)
self.critic_target = Critic(self.state_size, self.action_size)
# LOAD the weights
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 hyperparameters
self.exploration_mu = 0
self.exploration_theta = 0.35
self.exploration_sigma = 0.1
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Set the replay memory
self.buffer_size = 100000
self.batch_size = 32
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Update function hyperparameters
self.gamma = 0.99
self.tau = 0.001
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, task, train=True):
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
# Set the learning rate suggested by paper: https://pdfs.semanticscholar.org/71f2/03de1a53deae81a7707143f0ed564661e279.pdf
self.actor_learning_rate = 0.001
self.actor_decay = 0.0
self.critic_learning_rate = 0.001
self.critic_decay = 0.0
# Actor Model
self.actor_local = Actor(self.state_size, self.action_size, self.action_low, self.action_high, self.actor_learning_rate, self.actor_decay)
self.actor_target = Actor(self.state_size, self.action_size, self.action_low, self.action_high, self.actor_learning_rate, self.actor_decay)
# Critic Model
self.critic_local = Critic(self.state_size, self.action_size, self.critic_learning_rate, self.critic_decay)
self.critic_target = Critic(self.state_size, self.action_size, self.critic_learning_rate, self.critic_decay)
# initialize targets 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.exploration_theta = 0.01
self.exploration_sigma = 0.02
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)
self.best_w = None
self.best_score = -np.inf
# self.noise_scale = 0.7
self.score = 0
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.01 # for soft update of target parameters
# Indicate if we want to learn (or use to predict without learn)
self.set_train(train)
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,
task,
actor_learning_rate=0.001,
critic_learning_rate=0.001,
tau=0.01,
gamma=0.99,
buffer_size=100000,
batch_size=64,
exploration_mu=0,
exploration_theta=0.15,
exploration_sigma=0.2):
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,
actor_learning_rate)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high,
actor_learning_rate)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size,
critic_learning_rate)
self.critic_target = Critic(self.state_size, self.action_size,
critic_learning_rate)
# 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 = exploration_mu
self.exploration_theta = exploration_theta
self.exploration_sigma = exploration_sigma
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = buffer_size
self.batch_size = batch_size
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = gamma # discount factor
self.tau = tau # for soft update of target parameters
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.exploration_mu = 0.0001
#self.exploration_theta = 0.2
#self.exploration_sigma = 0.25
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
#self.tau = .005
self.closeCount = 0
# Score tracker #####################
self.best_score = -np.inf
self.total_reward = 0.0
self.currentclose = 0
def __init__(self, task, sess):
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.action_range = self.action_high - self.action_low
# Algorithm parameters
self.gamma = 0.9 # discount factor
self.tau = 2e-3 # for soft update of target parameters
self.actor_lr = 2e-3
self.critic_lr = 2e-3
# END
self.reward_variance = RunningVariance(1)
self.q_values_variance = RunningVariance(1)
# Actor (Policy) Model
self.actor_local = Actor(sess, self.state_size, self.action_size,
self.action_low, self.action_high,
self.actor_lr)
self.actor_target = Actor(sess, 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 = 0.8
self.exploration_sigma = 0.05 * self.action_range
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 100000
self.batch_size = 100
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# initialize
sess.run(tf.global_variables_initializer())
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
actor_local_params = actor_params()
actor_target_params = actor_params()
self.actor_local = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
params=actor_local_params)
self.actor_target = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
params=actor_target_params)
# Critic (Value) Model
critic_local_params = critic_params()
critic_target_params = critic_params()
self.critic_local = Critic(self.state_size,
self.action_size,
params=critic_local_params)
self.critic_target = Critic(self.state_size,
self.action_size,
params=critic_target_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())
agent_par = agent_params(self.action_size)
# Noise process
self.noise = agent_par.noise
# Replay memory
self.batch_size = agent_par.batch_size
self.memory = agent_par.memory
# Algorithm parameters
self.gamma = agent_par.gamma # discount factor
self.tau = agent_par.tau # for soft update of target parameters
# Compute the ongoing top score
self.top_score = -np.inf
self.score = 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, 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):
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.gamma = 1.0
#self.tau = 0.01 # for soft update of target parameters
self.tau = 0.001
self.epsilon = 1.0
self.epsilon_decay = 0.995
self.epsilon_min = 0.001
self.rotor_speed_min = 0
self.rotor_speed_max = 600
self.best_score = -np.inf
def setup(self, obs_spec, action_spec):
self.obs_spec = obs_spec
self.action_spec = action_spec
# can't do this
self.action_space = actions.ActionSpace
print('setting up agent with action space:', self.action_space)
self.w = np.random.normal(size=())
# number of different actions is pretty large: 541 different action id's
self.action_space = len(self.action_spec.functions._func_list)
# lets try to make the observation_space the feature_screen as a start
# maybe not all of them. dims are (17,84,84)
self.observation_space = self.obs_spec.feature_screen
#observation_space = obs_spec.player_relative
print('this is the obs_space for player_relative:',
self.observation_space)
print('made it!')
self.local_actor = Actor(state_size=self.observation_space,
action_size=self.action_space,
action_low=0,
action_high=541)
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, name, loadfile=False):
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)
self.name = name
if loadfile:
self.actor_local.model.load_weights("./weights/" + name +
"_actor.h5")
self.critic_local.model.load_weights("./weights/" + name +
"_critic.h5")
# 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 #0.3 #original 0.15
self.exploration_sigma = 0.3 #0.3 #original 0.3
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 1000000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.001 # for soft update of target parameters
def __init__(self, env):
self.env = env
self.state_size = self.env.observation_space.shape[0]
self.action_size = self.env.action_space.shape[0]
self.action_low = self.env.action_space.low[0]
self.action_high = self.env.action_space.high[0]
# Learning rates
self.actor_learning_rate = 1e-4
self.critic_learning_rate = 1e-3
# Actor (Policy) Model
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high,
self.actor_learning_rate)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high,
self.actor_learning_rate)
# Critic (Value) Model
self.critic_local = Critic(self.state_size, self.action_size,
self.critic_learning_rate)
self.critic_target = Critic(self.state_size, self.action_size,
self.critic_learning_rate)
# 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 = 0.1
self.exploration_sigma = 0.1
self.noise = OUNoise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
# Replay memory
self.buffer_size = 1000000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.001 # for soft update of target parameters
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.action_range = self.action_high - self.action_low
self.score = 0
# 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.action_low, self.action_high)
self.critic_target = Critic(self.state_size, self.action_size,
self.action_low, self.action_high)
# 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 # Taken from paper - changed from 10000 originally
self.batch_size = 64 # Taken from paper - changed from 64 originally
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.005 # Tau for soft update of target parameters. Taken from paper - changed from 0.01 originally
# Reset the episode when model set up
self.reset_episode()
def __init__(self, task):
# Task (environment) information
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.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
self.actor = Actor(self.state_size, self.action_size, self.action_low,
self.action_high)
self.critic = Critic(self.state_size, self.action_size)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high)
self.critic_target = Critic(self.state_size, self.action_size)
self.gamma = 0.95
self.tau = 0.001
self.best_w = None
self.best_score = -np.inf
self.exploration_mu = 0.5
self.exploration_theta = 0.2
self.exploration_sigma = 0.4
self.noise = Noise(self.action_size, self.exploration_mu,
self.exploration_theta, self.exploration_sigma)
self.buffer_size = 100000
self.batch_size = 32
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
self.best_score = -np.inf
self.num_steps = 0
# Episode variables
self.reset_episode()
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 = 0.001
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
# tuning gamma between (0.95 - 0.99)
self.gamma = 0.99 # discount factor
# tuning tau around (0.001 - 0.01)
self.tau = 0.005 # for soft update of target parameters
self.best_score = -np.inf
self.score = 0
self.step_count = 0
def __init__(self, task, exp_mu, exp_theta, exp_sigma, gamma, tau):
self.task = task
self.s_size = task.s_size
self.a_size = task.a_size
self.a_low = task.a_low
self.a_high = task.a_high
# Actor Model
self.actor_local = Actor(self.s_size, self.a_size, self.a_low,
self.a_high)
self.actor_target = Actor(self.s_size, self.a_size, self.a_low,
self.a_high)
# Critic Model
self.critic_local = Critic(self.s_size, self.a_size)
self.critic_target = Critic(self.s_size, self.a_size)
# Initialize target 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())
# initialize noise
self.exp_mu = exp_mu
self.exp_theta = exp_theta
self.exp_sigma = exp_sigma
self.noise = OUNoise(self.a_size, self.exp_mu, self.exp_theta,
self.exp_sigma)
# For Replay buffer
self.buff_size = 1024 * 1024
self.batch_size = 64
self.memory = ReplayBuffer(self.buff_size, self.batch_size)
# discount factor
self.gamma = gamma
# for soft update of target parameters
self.tau = tau
def __init__(self, task):
"""Initialize models"""
self.env = 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
# Initialize Actor (policy) models
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)
# Initialize Critic (value) models
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())
# 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 buffer
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size, self.batch_size)
# Algorithm parameters
self.gamma = 0.9 # discount factor
self.tau = 0.001 # for soft update of target parameters
def __init__(self, task, lra, lrc, db):
self.task = task
self.s_sz = task.state_size
self.a_sz = task.action_size
self.a_max = task.max_action
# Actor (Policy) Model
self.actor_local = Actor(self.s_sz, self.a_sz, lra)
self.actor_target = Actor(self.s_sz, self.a_sz, lra)
# First Critic (Value) Model
self.critic_local_1 = Critic(self.s_sz, self.a_sz, lrc)
self.critic_target_1 = Critic(self.s_sz, self.a_sz, lrc)
# Second Critic (Value) Model
self.critic_local_2 = Critic(self.s_sz, self.a_sz, lrc)
self.critic_target_2 = Critic(self.s_sz, self.a_sz, lrc)
# Initialize target model parameters with local model parameters
self.critic_target_1.model.set_weights(
self.critic_local_1.model.get_weights())
self.critic_target_2.model.set_weights(
self.critic_local_2.model.get_weights())
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
# Noise process
self.noise = GaussianNoise(self.a_sz)
# Replay memory
self.num_exp = 0
self.batch = 32
self.buffer = 10000
labels = ["state", "action", "reward", "next_state", "done"]
self.experience = namedtuple("Experience", field_names=labels)
self.memory = PrioritizedReplayBuffer(self.buffer, self.batch, db)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.005 # for soft update of target parameters
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):
self.reward = 0
self.episodes = 0
self.steps = 0
self.obs_spec = None
self.action_spec = None
self.w = None
self.action_space = 0
self.observation_space = 0
self.local_actor = Actor()
self.target_actor = None
self.local_critic = None
self.target_critic = None
def create_models(self,
hidden_sizes_actor=(512, 256),
hidden_sizes_critic=(512, 256, 256)):
self.actor_local = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
hidden_sizes=hidden_sizes_actor)
self.actor_target = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
hidden_sizes=hidden_sizes_actor)
self.actor_target.model.set_weights(
self.actor_local.model.get_weights())
self.critic_local = Critic(self.state_size,
self.action_size,
hidden_sizes=hidden_sizes_critic)
self.critic_target = Critic(self.state_size,
self.action_size,
hidden_sizes=hidden_sizes_critic)
self.critic_target.model.set_weights(
self.critic_local.model.get_weights())
