def run(self):
np.random.seed(1337)
max_episode = 400
max_step = 1000
max_explore_eps = 100.0
env = self.task
self.noise = OUNoise(self.action_size)
while (self.episode_num < max_episode):
state = env.reset().reshape(self.state_size)
action = self.act(state)
while (self.count < max_step):
env.render()
next_state, reward, done, info = env.step(action)
next_state = next_state.reshape(self.state_size)
reward = reward
action = self.step(state, reward, done)
state = next_state
if done:
break
def __init__(self, task):
# Task (environment) information
self.task = task
self.state_size = 3
self.action_size = 3
print("Original spaces:{}, {}\nConstrained spaces:{},{}".format(
self.task.observation_space.shape, self.task.action_space.shape,
self.state_size, self.action_size))
self.action_low = self.task.action_space.low
self.action_high = self.task.action_space.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 (Q-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.noise = OUNoise(self.action_size)
# Replay Buffer
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size)
# Policy parameters
self.gamma = 0.99
self.tau = 0.001
# Save episode stats
self.stats_filename = os.path.join(util.get_param('out'), "stats_{}.csv".format(util.get_timestamp()))
self.stats_columns = ["episode", "total_reward"]
self.episode_num = 1
# Episode variables
self.reset_episode_vars()
class DDPG(BaseAgent):
"""Sample agent that searches for optimal policy by DDPG. """
def __init__(self, task):
# Task (environment) information
self.task = task
self.state_size = 3
self.action_size = 3
print("Original spaces:{}, {}\nConstrained spaces:{},{}".format(
self.task.observation_space.shape, self.task.action_space.shape,
self.state_size, self.action_size))
self.action_low = self.task.action_space.low
self.action_high = self.task.action_space.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 (Q-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.noise = OUNoise(self.action_size)
# Replay Buffer
self.buffer_size = 100000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size)
# Policy parameters
self.gamma = 0.99
self.tau = 0.001
# Save episode stats
self.stats_filename = os.path.join(util.get_param('out'), "stats_{}.csv".format(util.get_timestamp()))
self.stats_columns = ["episode", "total_reward"]
self.episode_num = 1
# Episode variables
self.reset_episode_vars()
def preprocess_state(self, state):
""" Reduce state vector to relevant dimensions """
return state[0:3]
def preprocess_action(self, action):
""" Return complete action vector """
complete_action = np.zeros(self.task.action_space.shape)
complete_action[:3] = action
return complete_action
def reset_episode_vars(self):
self.last_state = None
self.last_action = None
self.total_reward = 0.0
self.count = 0
def write_stats(self, stats):
""" Write single episode stats to CSV file. """
df_stats = pd.DataFrame([stats], columns=self.stats_columns)
df_stats.to_csv(self.stats_filename, mode='a', index=False, header=not os.path.isfile(self.stats_filename))
def step(self, state, reward, done):
# preprocess state
state = self.preprocess_state(state)
# Transform state, choose action
action = self.act(state)
# Save experience
if self.last_state is not None and self.last_action is not None:
self.memory.push(self.last_state, self.last_action, reward, state, done)
self.total_reward += reward
self.count += 1
# Learn from buffer
if len(self.memory) > self.batch_size:
experiences = self.memory.sample(self.batch_size)
self.learn(experiences)
# Write episode stats
if done:
self.write_stats([self.episode_num, self.total_reward])
self.episode_num += 1
self.total_reward = 0.0
self.last_action = action
self.last_state = state
# Return complete action vector
return self.preprocess_action(action)
def act(self, states):
# Choose action based on given state and policy
states = np.reshape(states, [-1, self.state_size])
actions = self.actor_local.model.predict(states)
return actions + self.noise.sample() # add some noise for exploration
def learn(self, experiences):
""" Update policy and value parameters using given batch of experience tuples """
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
actions_next = self.actor_target.model.predict_on_batch(next_states)
Q_target_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_target_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())
# let the model more stable
new_weights = self.tau * local_weights + (1 - self.tau) * target_weights
target_model.set_weights(new_weights)
def __init__(self, task):
# Task (environment) information
self.task = task # should contain observation_space and action_space
self.load_weights = False
##############OVERRRIDE###############################
self.state_size = 6
self.action_size = 3
# self.state_size = self.task.observation_space.shape[0]
# self.action_size = self.task.action_space.shape[0]
######################################################
# Weights Saver
self.model_ext = ".h5"
self.save_weights_every = 100
if self.load_weights:
self.actor_filename = os.path.join(
util.get_param('out'),
"model_hover_2018-02-20_18-11-35_actor{}".format(
self.model_ext))
self.critic_filename = os.path.join(
util.get_param('out'),
"model_hover_2018-02-20_18-11-35_critic{}".format(
self.model_ext))
print("Actor filename :", self.actor_filename) # [debug]
print("Critic filename:", self.critic_filename) # [debug]
elif self.save_weights_every:
self.actor_filename = os.path.join(
util.get_param('out'),
"model_{}_actor{}".format(util.get_timestamp(),
self.model_ext))
self.critic_filename = os.path.join(
util.get_param('out'),
"model_{}_critic{}".format(util.get_timestamp(),
self.model_ext))
print("Actor filename :", self.actor_filename) # [debug]
print("Critic filename:", self.critic_filename) # [debug]
print('State Size : {}, Action Size : {}'.format(
self.state_size, self.action_size))
self.action_high = self.task.action_space.high[0:self.action_size]
self.action_low = self.task.action_space.low[0:self.action_size]
print('Action LOW : {}, Action HIGH : {}'.format(
self.action_low, self.action_high))
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high, 0.001)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high, 0.001)
self.critic_local = Critic(self.state_size, self.action_size, 0.001)
self.critic_target = Critic(self.state_size, self.action_size, 0.001)
# Load pre-trained model weights, if available
if self.load_weights and os.path.isfile(self.actor_filename):
try:
self.actor_local.model.load_weights(self.actor_filename)
self.critic_local.model.load_weights(self.critic_filename)
print("Model weights loaded from file!") # [debug]
except Exception as e:
print("Unable to load model weights from file!")
print("{}: {}".format(e.__class__.__name__, str(e)))
# Policy 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.buffer_size = 10000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.001 # for soft update of target parameters
self.noise = OUNoise(self.action_size)
# Stats Writer
self.stats_filename = os.path.join(
util.get_param('out'),
"stats_{}.csv".format(util.get_timestamp())) # path to CSV file
self.stats_columns = ['episode',
'avg_reward'] # specify columns to save
print("Saving stats {} to {}".format(self.stats_columns,
self.stats_filename)) # [debug]
self.episode_num = 0
self.reset_episode_vars()
class DDPG_H(BaseAgent):
"""Sample agent that searches for optimal policy randomly."""
def __init__(self, task):
# Task (environment) information
self.task = task # should contain observation_space and action_space
self.load_weights = False
##############OVERRRIDE###############################
self.state_size = 6
self.action_size = 3
# self.state_size = self.task.observation_space.shape[0]
# self.action_size = self.task.action_space.shape[0]
######################################################
# Weights Saver
self.model_ext = ".h5"
self.save_weights_every = 100
if self.load_weights:
self.actor_filename = os.path.join(
util.get_param('out'),
"model_hover_2018-02-20_18-11-35_actor{}".format(
self.model_ext))
self.critic_filename = os.path.join(
util.get_param('out'),
"model_hover_2018-02-20_18-11-35_critic{}".format(
self.model_ext))
print("Actor filename :", self.actor_filename) # [debug]
print("Critic filename:", self.critic_filename) # [debug]
elif self.save_weights_every:
self.actor_filename = os.path.join(
util.get_param('out'),
"model_{}_actor{}".format(util.get_timestamp(),
self.model_ext))
self.critic_filename = os.path.join(
util.get_param('out'),
"model_{}_critic{}".format(util.get_timestamp(),
self.model_ext))
print("Actor filename :", self.actor_filename) # [debug]
print("Critic filename:", self.critic_filename) # [debug]
print('State Size : {}, Action Size : {}'.format(
self.state_size, self.action_size))
self.action_high = self.task.action_space.high[0:self.action_size]
self.action_low = self.task.action_space.low[0:self.action_size]
print('Action LOW : {}, Action HIGH : {}'.format(
self.action_low, self.action_high))
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high, 0.001)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high, 0.001)
self.critic_local = Critic(self.state_size, self.action_size, 0.001)
self.critic_target = Critic(self.state_size, self.action_size, 0.001)
# Load pre-trained model weights, if available
if self.load_weights and os.path.isfile(self.actor_filename):
try:
self.actor_local.model.load_weights(self.actor_filename)
self.critic_local.model.load_weights(self.critic_filename)
print("Model weights loaded from file!") # [debug]
except Exception as e:
print("Unable to load model weights from file!")
print("{}: {}".format(e.__class__.__name__, str(e)))
# Policy 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.buffer_size = 10000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.001 # for soft update of target parameters
self.noise = OUNoise(self.action_size)
# Stats Writer
self.stats_filename = os.path.join(
util.get_param('out'),
"stats_{}.csv".format(util.get_timestamp())) # path to CSV file
self.stats_columns = ['episode',
'avg_reward'] # specify columns to save
print("Saving stats {} to {}".format(self.stats_columns,
self.stats_filename)) # [debug]
self.episode_num = 0
self.reset_episode_vars()
def run(self):
np.random.seed(1337)
max_episode = 400
max_step = 1000
max_explore_eps = 100.0
env = self.task
self.noise = OUNoise(self.action_size)
while (self.episode_num < max_episode):
state = env.reset().reshape(self.state_size)
action = self.act(state)
while (self.count < max_step):
env.render()
next_state, reward, done, info = env.step(action)
next_state = next_state.reshape(self.state_size)
reward = reward
action = self.step(state, reward, done)
state = next_state
if done:
break
def reset_episode_vars(self):
self.last_state = None
self.last_action = None
self.total_reward = 0.0
self.count = 0
self.actor_loss = 0.0
self.critic_loss = 0.0
self.episode_num += 1
def step(self, state, reward, done):
self.count += 1
# Save exp
if (self.last_state is not None):
self.memory.add(self.last_state, self.last_action, reward, state,
done)
if (self.batch_size < self.memory.length()):
# Learn exp
experinces = self.memory.sample(self.batch_size)
self.learn(experinces)
self.total_reward += reward
self.last_state = state
if (done):
self.actor_loss /= float(self.count)
self.critic_loss /= float(self.count)
print('Loss, Actor {:4f}, Critic {:4f}'.format(
self.actor_loss, self.critic_loss))
print('Episode {}, Score {:8f}, Steps {}, Normalize Score {:4f}'.
format(self.episode_num, self.total_reward, self.count,
self.total_reward / float(self.count)))
# Write episode stats
self.write_stats(
[self.episode_num, (self.total_reward / float(self.count))])
# Save model weights at regular intervals
if (self.episode_num % self.save_weights_every == 0):
self.actor_local.model.save_weights(self.actor_filename)
self.critic_local.model.save_weights(self.critic_filename)
print("Model weights saved at episode",
self.episode_num) # [debug]
self.reset_episode_vars()
return
action = self.act(state)
self.last_action = action
action = self.postprocess_action(action)
return action
def act(self, states):
states = states.reshape(1, self.state_size)
action = self.actor_local.model.predict(states)[0]
# action = self.actor.model.predict(states)
return (action + self.noise.sample())
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.array([e.state for e in experiences])
actions = np.array([e.action for e in experiences])
rewards = np.array([e.reward for e in experiences])
dones = np.array([e.done for e in experiences])
next_states = np.array([e.next_state for e in experiences])
# 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.reshape(len(experiences)) *
(1 - dones))
self.critic_loss += self.critic_local.model.train_on_batch(
x=[states, actions], y=Q_targets)
actions_for_grads = self.actor_local.model.predict(states)
action_gradients = np.reshape(
self.critic_local.get_action_gradients(
[states, actions_for_grads, 0]), (-1, self.action_size))
self.actor_loss += self.actor_local.train_fn(
[states, action_gradients, 1])[0] # 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())
new_weights = self.tau * local_weights + (
(1 - self.tau) * target_weights)
target_model.set_weights(new_weights)
def write_stats(self, stats):
"""Write single episode stats to CSV file."""
df_stats = pd.DataFrame(
[stats], columns=self.stats_columns) # single-row dataframe
df_stats.to_csv(
self.stats_filename,
mode='a',
index=False,
header=not os.path.isfile(
self.stats_filename)) # write header first time only
def preprocess_state(self, state):
"""Reduce state vector to relevant dimensions."""
return state[0:3] # position only
def postprocess_action(self, action):
"""Return complete action vector."""
complete_action = np.zeros(self.task.action_space.shape) # shape: (6,)
complete_action[0:3] = action # linear force only
return complete_action
def __init__(self, task):
# Task (environment) information
self.task = task # should contain observation_space and action_space
##############OVERRRIDE###############################
self.state_size = 3
self.action_size = 3
# self.state_size = self.task.observation_space.shape[0]
# self.action_size = self.task.action_space.shape[0]
######################################################
print('State Size : {}, Action Size : {}'.format(
self.state_size, self.action_size))
self.action_high = self.task.action_space.high[0:self.action_size]
self.action_low = self.task.action_space.low[0:self.action_size]
print('Action LOW : {}, Action HIGH : {}'.format(
self.action_low, self.action_high))
self.actor_local = Actor(self.state_size, self.action_size,
self.action_low, self.action_high, 0.0001)
self.actor_target = Actor(self.state_size, self.action_size,
self.action_low, self.action_high, 0.0001)
self.critic_local = Critic(self.state_size, self.action_size, 0.001)
self.critic_target = Critic(self.state_size, self.action_size, 0.001)
# Policy 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.buffer_size = 10000
self.batch_size = 64
self.memory = ReplayBuffer(self.buffer_size)
# Algorithm parameters
self.gamma = 0.99 # discount factor
self.tau = 0.001 # for soft update of target parameters
self.noise = OUNoise(self.action_size)
# Stats Writer
self.stats_filename = os.path.join(
util.get_param('out'),
"stats_{}.csv".format(util.get_timestamp())) # path to CSV file
self.stats_columns = ['episode',
'total_reward'] # specify columns to save
print("Saving stats {} to {}".format(self.stats_columns,
self.stats_filename)) # [debug]
# Weights Saver
self.model_ext = ".h5"
self.save_weights_every = 100
if self.save_weights_every:
self.actor_filename = os.path.join(
util.get_param('out'),
"{}_actor{}".format(util.get_timestamp(), self.model_ext))
self.critic_filename = os.path.join(
util.get_param('out'),
"{}_critic{}".format(util.get_timestamp(), self.model_ext))
print("Actor filename :", self.actor_filename) # [debug]
print("Critic filename:", self.critic_filename) # [debug]
self.episode_num = 0
self.reset_episode_vars()