def __init__(self,
task,
exploration_mu=0,
exploration_theta=0.15,
exploration_sigma=0.2,
buffer_size=100000,
batch_size=64,
gamma=0.99,
tau=0.01,
actor_learning_rate=0.001,
critic_learning_rate=0.001):
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=actor_learning_rate)
self.actor_target = Actor(self.state_size,
self.action_size,
self.action_low,
self.action_high,
learning_rate=actor_learning_rate)
# Critic (Value) Model
self.critic_local = Critic(self.state_size,
self.action_size,
learning_rate=critic_learning_rate)
self.critic_target = Critic(self.state_size,
self.action_size,
learning_rate=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
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
# 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 = 0.005
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
# Discount factor
self.gamma = 0.99
# For soft update of target parameters
self.tau = 0.15
self.best_score = -np.inf
self.score = 0
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
# 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
# self.last_state = None
self.best_w = None
self.best_score = -np.inf
self.noise_scale = 0.1
self.score = 0
self.total_reward = None
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())
# The reference for the values below can be seen in the DDPG paper
# "Continuous control with deep reinforcement learning"
# 7. EXPERIMENT DETAILS
#
# This
# 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 = 0.001 # for soft update of target parameters
# Tracking
self.score = -np.inf
self.best_score = -np.inf
