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 AE_DDPG_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
# 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()
def reset_episode(self):
self.noise.reset()
state = self.task.reset()
self.last_state = state
self.best_score = -np.inf
self.score = -np.inf
self.total_reward = 0.0
self.count = 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
self.total_reward += reward
self.count += 1
# AE: Score (average reward in this episode so far) and best score for statistics
self.score = self.total_reward / float(self.count)
if self.score > self.best_score:
self.best_score = self.score
def act(self, state):
"""Returns actions for given state(s) as per current policy."""
state = np.reshape(state, [-1, self.state_size])
# AE: directly sampling approximated value from learned action-value function.
action = self.actor_local.model.predict(state)[0]
# AE: and adding some noise to that for unpredictability.
# AE: The magnitude of noise has to drop over time.
explore_p = self.explore_stop + (self.explore_start -
self.explore_stop) * np.exp(
-self.decay_rate * self.count)
#self.noise.update_mu(explore_p)
noise_sample = self.magnitude_coeff * explore_p * self.noise.sample()
#noise_sample = explore_p * np.random.randn(self.action_size)
#print("Noi=", s)
return list(
action +
noise_sample * self.action_size) # 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"
# AE: Updating NN weights directly in the passed model (actor or critic).
new_weights = self.tau * local_weights + (1 -
self.tau) * target_weights
target_model.set_weights(new_weights)
class DDPG():
"""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
self.exploration_sigma = 0.3
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
def reset_episode(self):
self.noise.reset()
state = self.task.reset()
self.last_state = state
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.batch_size)
# self.experiences.append(experiences)
self.learn(experiences)
# Roll over last state and action
self.last_state = next_state
# return experiences
def act(self, state):
"""Returns actions for given state(s) as per current policy."""
state = np.reshape(state, [-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.)
# print("Learning")
# print(len(experiences))
states = np.vstack([e.state for e in experiences if e is not None])
# print(len(states))
actions = np.array([e.action for e in experiences
if e is not None]).astype(np.float32).reshape(
-1, self.action_size)
# print(len(actions))
rewards = np.array([e.reward for e in experiences if e is not None
]).astype(np.float32).reshape(-1, 1)
# print("States/Actions: {} {}".format(len(states),len(actions)))
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)
# return len(states), len(actions)
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)