class SARSALambdaContinuous(TD):
"""
Continuous version of SARSA(lambda) algorithm.
"""
def __init__(self, approximator, policy, mdp_info, params, features):
self.Q = Regressor(approximator, **params['approximator_params'])
self.e = np.zeros(self.Q.weights_size)
self._lambda = params['algorithm_params']['lambda']
super(SARSALambdaContinuous, self).__init__(self.Q, policy, mdp_info,
params, features)
def _update(self, state, action, reward, next_state, absorbing):
phi_state = self.phi(state)
q_current = self.Q.predict(phi_state, action)
alpha = self.alpha(state, action)
self.e = self.mdp_info.gamma * self._lambda * self.e + self.Q.diff(
phi_state, action)
self._next_action = self.draw_action(next_state)
phi_next_state = self.phi(next_state)
q_next = self.Q.predict(phi_next_state,
self._next_action) if not absorbing else 0.
delta = reward + self.mdp_info.gamma * q_next - q_current
theta = self.Q.get_weights()
theta += alpha * delta * self.e
self.Q.set_weights(theta)
def episode_start(self):
self.e = np.zeros(self.Q.weights_size)
class TrueOnlineSARSALambda(TD):
"""
True Online SARSA(lambda) with linear function approximation.
"True Online TD(lambda)". Seijen H. V. et al.. 2014.
"""
def __init__(self, policy, mdp_info, learning_rate, lambda_coeff,
features, approximator_params=None):
"""
Constructor.
Args:
lambda_coeff (float): eligibility trace coefficient.
"""
self._approximator_params = dict() if approximator_params is None else \
approximator_params
self.Q = Regressor(LinearApproximator, **self._approximator_params)
self.e = np.zeros(self.Q.weights_size)
self._lambda = lambda_coeff
self._q_old = None
super(TrueOnlineSARSALambda, self).__init__(self.Q, policy, mdp_info,
learning_rate, features)
def _update(self, state, action, reward, next_state, absorbing):
phi_state = self.phi(state)
phi_state_action = get_action_features(phi_state, action,
self.mdp_info.action_space.n)
q_current = self.Q.predict(phi_state, action)
if self._q_old is None:
self._q_old = q_current
alpha = self.alpha(state, action)
e_phi = self.e.dot(phi_state_action)
self.e = self.mdp_info.gamma * self._lambda * self.e + alpha * (
1. - self.mdp_info.gamma * self._lambda * e_phi) * phi_state_action
self.next_action = self.draw_action(next_state)
phi_next_state = self.phi(next_state)
q_next = self.Q.predict(phi_next_state,
self.next_action) if not absorbing else 0.
delta = reward + self.mdp_info.gamma * q_next - self._q_old
theta = self.Q.get_weights()
theta += delta * self.e + alpha * (
self._q_old - q_current) * phi_state_action
self.Q.set_weights(theta)
self._q_old = q_next
def episode_start(self):
self._q_old = None
self.e = np.zeros(self.Q.weights_size)
class SARSALambdaContinuous(TD):
"""
Continuous version of SARSA(lambda) algorithm.
"""
def __init__(self,
approximator,
policy,
mdp_info,
learning_rate,
lambda_coeff,
features,
approximator_params=None):
"""
Constructor.
Args:
lambda_coeff (float): eligibility trace coefficient.
"""
self._approximator_params = dict() if approximator_params is None else \
approximator_params
self.Q = Regressor(approximator, **self._approximator_params)
self.e = np.zeros(self.Q.weights_size)
self._lambda = lambda_coeff
super().__init__(self.Q, policy, mdp_info, learning_rate, features)
def _update(self, state, action, reward, next_state, absorbing):
phi_state = self.phi(state)
q_current = self.Q.predict(phi_state, action)
alpha = self.alpha(state, action)
self.e = self.mdp_info.gamma * self._lambda * self.e + self.Q.diff(
phi_state, action)
self.next_action = self.draw_action(next_state)
phi_next_state = self.phi(next_state)
q_next = self.Q.predict(phi_next_state,
self.next_action) if not absorbing else 0.
delta = reward + self.mdp_info.gamma * q_next - q_current
theta = self.Q.get_weights()
theta += alpha * delta * self.e
self.Q.set_weights(theta)
def episode_start(self):
self.e = np.zeros(self.Q.weights_size)
super().episode_start()
class TrueOnlineSARSALambda(TD):
"""
True Online SARSA(lambda) with linear function approximation.
"True Online TD(lambda)". Seijen H. V. et al.. 2014.
"""
def __init__(self, policy, mdp_info, params, features):
self.Q = Regressor(LinearApproximator, **params['approximator_params'])
self.e = np.zeros(self.Q.weights_size)
self._lambda = params['algorithm_params']['lambda']
self._q_old = None
super(TrueOnlineSARSALambda, self).__init__(self.Q, policy, mdp_info,
params, features)
def _update(self, state, action, reward, next_state, absorbing):
phi_state = self.phi(state)
phi_state_action = get_action_features(phi_state, action,
self.mdp_info.action_space.n)
q_current = self.Q.predict(phi_state, action)
if self._q_old is None:
self._q_old = q_current
alpha = self.alpha(state, action)
e_phi = self.e.dot(phi_state_action)
self.e = self.mdp_info.gamma * self._lambda * self.e + alpha * (
1. - self.mdp_info.gamma * self._lambda * e_phi) * phi_state_action
self._next_action = self.draw_action(next_state)
phi_next_state = self.phi(next_state)
q_next = self.Q.predict(phi_next_state,
self._next_action) if not absorbing else 0.
delta = reward + self.mdp_info.gamma * q_next - self._q_old
theta = self.Q.get_weights()
theta += delta * self.e + alpha * (self._q_old -
q_current) * phi_state_action
self.Q.set_weights(theta)
self._q_old = q_next
def episode_start(self):
self._q_old = None
self.e = np.zeros(self.Q.weights_size)
class SARSALambdaContinuous(TD):
"""
Continuous version of SARSA(lambda) algorithm.
"""
def __init__(self, approximator, policy, mdp_info, learning_rate,
lambda_coeff, features, approximator_params=None):
"""
Constructor.
Args:
lambda_coeff (float): eligibility trace coefficient.
"""
self._approximator_params = dict() if approximator_params is None else \
approximator_params
self.Q = Regressor(approximator, **self._approximator_params)
self.e = np.zeros(self.Q.weights_size)
self._lambda = lambda_coeff
super(SARSALambdaContinuous, self).__init__(self.Q, policy, mdp_info,
learning_rate, features)
def _update(self, state, action, reward, next_state, absorbing):
phi_state = self.phi(state)
q_current = self.Q.predict(phi_state, action)
alpha = self.alpha(state, action)
self.e = self.mdp_info.gamma * self._lambda * self.e + self.Q.diff(
phi_state, action)
self.next_action = self.draw_action(next_state)
phi_next_state = self.phi(next_state)
q_next = self.Q.predict(phi_next_state,
self.next_action) if not absorbing else 0.
delta = reward + self.mdp_info.gamma * q_next - q_current
theta = self.Q.get_weights()
theta += alpha * delta * self.e
self.Q.set_weights(theta)
def episode_start(self):
self.e = np.zeros(self.Q.weights_size)
class TrueOnlineSARSALambda(TD):
"""
True Online SARSA(lambda) with linear function approximation.
"True Online TD(lambda)". Seijen H. V. et al.. 2014.
"""
def __init__(self,
policy,
mdp_info,
learning_rate,
lambda_coeff,
features,
approximator_params=None):
"""
Constructor.
Args:
lambda_coeff (float): eligibility trace coefficient.
"""
self._approximator_params = dict() if approximator_params is None else \
approximator_params
self.Q = Regressor(LinearApproximator, **self._approximator_params)
self.e = np.zeros(self.Q.weights_size)
self._lambda = lambda_coeff
self._q_old = None
super().__init__(self.Q, policy, mdp_info, learning_rate, features)
def _update(self, state, action, reward, next_state, absorbing):
phi_state = self.phi(state)
phi_state_action = get_action_features(phi_state, action,
self.mdp_info.action_space.n)
q_current = self.Q.predict(phi_state, action)
if self._q_old is None:
self._q_old = q_current
alpha = self.alpha(state, action)
e_phi = self.e.dot(phi_state_action)
self.e = self.mdp_info.gamma * self._lambda * self.e + alpha * (
1. - self.mdp_info.gamma * self._lambda * e_phi) * phi_state_action
self.next_action = self.draw_action(next_state)
phi_next_state = self.phi(next_state)
q_next = self.Q.predict(phi_next_state,
self.next_action) if not absorbing else 0.
delta = reward + self.mdp_info.gamma * q_next - self._q_old
theta = self.Q.get_weights()
theta += delta * self.e + alpha * (self._q_old -
q_current) * phi_state_action
self.Q.set_weights(theta)
self._q_old = q_next
def episode_start(self):
self._q_old = None
self.e = np.zeros(self.Q.weights_size)
super().episode_start()
class DDPG(Agent):
"""
Deep Deterministic Policy Gradient algorithm.
"Continuous Control with Deep Reinforcement Learning".
Lillicrap T. P. et al.. 2016.
"""
def __init__(self, actor_approximator, critic_approximator, policy_class,
mdp_info, batch_size, initial_replay_size, max_replay_size,
tau, actor_params, critic_params, policy_params,
actor_fit_params=None, critic_fit_params=None):
"""
Constructor.
Args:
actor_approximator (object): the approximator to use for the actor;
critic_approximator (object): the approximator to use for the
critic;
policy_class (Policy): class of the policy;
batch_size (int): the number of samples in a batch;
initial_replay_size (int): the number of samples to collect before
starting the learning;
max_replay_size (int): the maximum number of samples in the replay
memory;
tau (float): value of coefficient for soft updates;
actor_params (dict): parameters of the actor approximator to
build;
critic_params (dict): parameters of the critic approximator to
build;
policy_params (dict): parameters of the policy to build;
actor_fit_params (dict, None): parameters of the fitting algorithm
of the actor approximator;
critic_fit_params (dict, None): parameters of the fitting algorithm
of the critic approximator;
"""
self._actor_fit_params = dict() if actor_fit_params is None else actor_fit_params
self._critic_fit_params = dict() if critic_fit_params is None else critic_fit_params
self._batch_size = batch_size
self._tau = tau
self._replay_memory = ReplayMemory(initial_replay_size, max_replay_size)
target_critic_params = deepcopy(critic_params)
self._critic_approximator = Regressor(critic_approximator,
**critic_params)
self._target_critic_approximator = Regressor(critic_approximator,
**target_critic_params)
if 'loss' not in actor_params:
actor_params['loss'] = ActorLoss(self._critic_approximator)
target_actor_params = deepcopy(actor_params)
self._actor_approximator = Regressor(actor_approximator,
**actor_params)
self._target_actor_approximator = Regressor(actor_approximator,
**target_actor_params)
self._target_actor_approximator.model.set_weights(
self._actor_approximator.model.get_weights())
self._target_critic_approximator.model.set_weights(
self._critic_approximator.model.get_weights())
policy = policy_class(self._actor_approximator, **policy_params)
super().__init__(policy, mdp_info)
def fit(self, dataset):
self._replay_memory.add(dataset)
if self._replay_memory.initialized:
state, action, reward, next_state, absorbing, _ =\
self._replay_memory.get(self._batch_size)
q_next = self._next_q(next_state, absorbing)
q = reward + self.mdp_info.gamma * q_next
self._critic_approximator.fit(state, action, q,
**self._critic_fit_params)
self._actor_approximator.fit(state, state,
**self._actor_fit_params)
self._update_target()
def _update_target(self):
"""
Update the target networks.
"""
critic_weights = self._tau * self._critic_approximator.model.get_weights()
critic_weights += (1 - self._tau) * self._target_critic_approximator.get_weights()
self._target_critic_approximator.set_weights(critic_weights)
actor_weights = self._tau * self._actor_approximator.model.get_weights()
actor_weights += (1 - self._tau) * self._target_actor_approximator.get_weights()
self._target_actor_approximator.set_weights(actor_weights)
def _next_q(self, next_state, absorbing):
"""
Args:
next_state (np.ndarray): the states where next action has to be
evaluated;
absorbing (np.ndarray): the absorbing flag for the states in
``next_state``.
Returns:
Action-values returned by the critic for ``next_state`` and the
action returned by the actor.
"""
a = self._target_actor_approximator(next_state)
q = self._target_critic_approximator.predict(next_state, a)
q *= 1 - absorbing
return q
import numpy as np
from matplotlib import pyplot as plt
from mushroom.approximators import Regressor
from mushroom.approximators.parametric import LinearApproximator
x = np.arange(10).reshape(-1, 1)
intercept = 10
noise = np.random.randn(10, 1) * 1
y = 2 * x + intercept + noise
phi = np.concatenate((np.ones(10).reshape(-1, 1), x), axis=1)
regressor = Regressor(LinearApproximator,
input_shape=(2,),
output_shape=(1,))
regressor.fit(phi, y)
print('Weights: ' + str(regressor.get_weights()))
print('Gradient: ' + str(regressor.diff(np.array([[5.]]))))
plt.scatter(x, y)
plt.plot(x, regressor.predict(phi))
plt.show()
import numpy as np
from matplotlib import pyplot as plt
from mushroom.approximators import Regressor
from mushroom.approximators.parametric import LinearApproximator
x = np.arange(10).reshape(-1, 1)
intercept = 10
noise = np.random.randn(10, 1) * 1
y = 2 * x + intercept + noise
phi = np.concatenate((np.ones(10).reshape(-1, 1), x), axis=1)
regressor = Regressor(LinearApproximator,
input_shape=(2, ),
output_shape=(1, ))
regressor.fit(phi, y)
print('Weights: ' + str(regressor.get_weights()))
print('Gradient: ' + str(regressor.diff(np.array([[5.]]))))
plt.scatter(x, y)
plt.plot(x, regressor.predict(phi))
plt.show()
class DDPG(ReparametrizationAC):
"""
Deep Deterministic Policy Gradient algorithm.
"Continuous Control with Deep Reinforcement Learning".
Lillicrap T. P. et al.. 2016.
"""
def __init__(self, mdp_info, policy_class, policy_params,
batch_size, initial_replay_size, max_replay_size,
tau, critic_params, actor_params, actor_optimizer,
policy_delay=1, critic_fit_params=None):
"""
Constructor.
Args:
policy_class (Policy): class of the policy;
policy_params (dict): parameters of the policy to build;
batch_size (int): the number of samples in a batch;
initial_replay_size (int): the number of samples to collect before
starting the learning;
max_replay_size (int): the maximum number of samples in the replay
memory;
tau (float): value of coefficient for soft updates;
actor_params (dict): parameters of the actor approximator to
build;
critic_params (dict): parameters of the critic approximator to
build;
actor_optimizer (dict): parameters to specify the actor optimizer
algorithm;
policy_delay (int, 1): the number of updates of the critic after
which an actor update is implemented;
critic_fit_params (dict, None): parameters of the fitting algorithm
of the critic approximator;
"""
self._critic_fit_params = dict() if critic_fit_params is None else critic_fit_params
self._batch_size = batch_size
self._tau = tau
self._policy_delay = policy_delay
self._fit_count = 0
self._replay_memory = ReplayMemory(initial_replay_size, max_replay_size)
target_critic_params = deepcopy(critic_params)
self._critic_approximator = Regressor(TorchApproximator,
**critic_params)
self._target_critic_approximator = Regressor(TorchApproximator,
**target_critic_params)
target_actor_params = deepcopy(actor_params)
self._actor_approximator = Regressor(TorchApproximator,
**actor_params)
self._target_actor_approximator = Regressor(TorchApproximator,
**target_actor_params)
self._init_target()
policy = policy_class(self._actor_approximator, **policy_params)
policy_parameters = self._actor_approximator.model.network.parameters()
super().__init__(policy, mdp_info, actor_optimizer, policy_parameters)
def fit(self, dataset):
self._replay_memory.add(dataset)
if self._replay_memory.initialized:
state, action, reward, next_state, absorbing, _ =\
self._replay_memory.get(self._batch_size)
q_next = self._next_q(next_state, absorbing)
q = reward + self.mdp_info.gamma * q_next
self._critic_approximator.fit(state, action, q,
**self._critic_fit_params)
if self._fit_count % self._policy_delay == 0:
loss = self._loss(state)
self._optimize_actor_parameters(loss)
self._update_target()
self._fit_count += 1
def _loss(self, state):
action = self._actor_approximator(state, output_tensor=True)
q = self._critic_approximator(state, action, output_tensor=True)
return -q.mean()
def _init_target(self):
"""
Init weights for target approximators
"""
self._target_actor_approximator.set_weights(
self._actor_approximator.get_weights())
self._target_critic_approximator.set_weights(
self._critic_approximator.get_weights())
def _update_target(self):
"""
Update the target networks.
"""
critic_weights = self._tau * self._critic_approximator.get_weights()
critic_weights += (1 - self._tau) * self._target_critic_approximator.get_weights()
self._target_critic_approximator.set_weights(critic_weights)
actor_weights = self._tau * self._actor_approximator.get_weights()
actor_weights += (1 - self._tau) * self._target_actor_approximator.get_weights()
self._target_actor_approximator.set_weights(actor_weights)
def _next_q(self, next_state, absorbing):
"""
Args:
next_state (np.ndarray): the states where next action has to be
evaluated;
absorbing (np.ndarray): the absorbing flag for the states in
``next_state``.
Returns:
Action-values returned by the critic for ``next_state`` and the
action returned by the actor.
"""
a = self._target_actor_approximator(next_state)
q = self._target_critic_approximator.predict(next_state, a)
q *= 1 - absorbing
return q
class SAC(ReparametrizationAC):
"""
Soft Actor-Critic algorithm.
"Soft Actor-Critic Algorithms and Applications".
Haarnoja T. et al.. 2019.
"""
def __init__(self, mdp_info,
batch_size, initial_replay_size, max_replay_size,
warmup_transitions, tau, lr_alpha,
actor_mu_params, actor_sigma_params,
actor_optimizer, critic_params,
target_entropy=None, critic_fit_params=None):
"""
Constructor.
Args:
batch_size (int): the number of samples in a batch;
initial_replay_size (int): the number of samples to collect before
starting the learning;
max_replay_size (int): the maximum number of samples in the replay
memory;
warmup_transitions (int): number of samples to accumulate in the
replay memory to start the policy fitting;
tau (float): value of coefficient for soft updates;
lr_alpha (float): Learning rate for the entropy coefficient;
actor_mu_params (dict): parameters of the actor mean approximator
to build;
actor_sigma_params (dict): parameters of the actor sigma approximator
to build;
actor_optimizer (dict): parameters to specify the actor
optimizer algorithm;
critic_params (dict): parameters of the critic approximator to
build;
target_entropy (float, None): target entropy for the policy, if None
a default value is computed ;
critic_fit_params (dict, None): parameters of the fitting algorithm
of the critic approximator.
"""
self._critic_fit_params = dict() if critic_fit_params is None else critic_fit_params
self._batch_size = batch_size
self._warmup_transitions = warmup_transitions
self._tau = tau
if target_entropy is None:
self._target_entropy = -np.prod(mdp_info.action_space.shape).astype(np.float32)
else:
self._target_entropy = target_entropy
self._replay_memory = ReplayMemory(initial_replay_size, max_replay_size)
if 'n_models' in critic_params.keys():
assert critic_params['n_models'] == 2
else:
critic_params['n_models'] = 2
if 'prediction' in critic_params.keys():
assert critic_params['prediction'] == 'min'
else:
critic_params['prediction'] = 'min'
target_critic_params = deepcopy(critic_params)
self._critic_approximator = Regressor(TorchApproximator,
**critic_params)
self._target_critic_approximator = Regressor(TorchApproximator,
**target_critic_params)
self._log_alpha = torch.tensor(0., requires_grad=True, dtype=torch.float32)
self._alpha_optim = optim.Adam([self._log_alpha], lr=lr_alpha)
actor_mu_approximator = Regressor(TorchApproximator,
**actor_mu_params)
actor_sigma_approximator = Regressor(TorchApproximator,
**actor_sigma_params)
policy = SACPolicy(actor_mu_approximator,
actor_sigma_approximator,
mdp_info.action_space.low,
mdp_info.action_space.high)
self._init_target()
policy_parameters = chain(actor_mu_approximator.model.network.parameters(),
actor_sigma_approximator.model.network.parameters())
super().__init__(policy, mdp_info, actor_optimizer, policy_parameters)
def fit(self, dataset):
self._replay_memory.add(dataset)
if self._replay_memory.initialized:
state, action, reward, next_state, absorbing, _ = \
self._replay_memory.get(self._batch_size)
if self._replay_memory.size > self._warmup_transitions:
action_new, log_prob = self.policy.compute_action_and_log_prob_t(state)
loss = self._loss(state, action_new, log_prob)
self._optimize_actor_parameters(loss)
self._update_alpha(log_prob.detach())
q_next = self._next_q(next_state, absorbing)
q = reward + self.mdp_info.gamma * q_next
self._critic_approximator.fit(state, action, q,
**self._critic_fit_params)
self._update_target()
def _init_target(self):
"""
Init weights for target approximators.
"""
for i in range(len(self._critic_approximator)):
self._target_critic_approximator.model[i].set_weights(
self._critic_approximator.model[i].get_weights())
def _loss(self, state, action_new, log_prob):
q_0 = self._critic_approximator(state, action_new,
output_tensor=True, idx=0)
q_1 = self._critic_approximator(state, action_new,
output_tensor=True, idx=1)
q = torch.min(q_0, q_1)
return (self._alpha * log_prob - q).mean()
def _update_alpha(self, log_prob):
alpha_loss = - (self._log_alpha * (log_prob + self._target_entropy)).mean()
self._alpha_optim.zero_grad()
alpha_loss.backward()
self._alpha_optim.step()
def _update_target(self):
"""
Update the target networks.
"""
for i in range(len(self._target_critic_approximator)):
critic_weights_i = self._tau * self._critic_approximator.model[i].get_weights()
critic_weights_i += (1 - self._tau) * self._target_critic_approximator.model[i].get_weights()
self._target_critic_approximator.model[i].set_weights(critic_weights_i)
def _next_q(self, next_state, absorbing):
"""
Args:
next_state (np.ndarray): the states where next action has to be
evaluated;
absorbing (np.ndarray): the absorbing flag for the states in
``next_state``.
Returns:
Action-values returned by the critic for ``next_state`` and the
action returned by the actor.
"""
a, log_prob_next = self.policy.compute_action_and_log_prob(next_state)
q = self._target_critic_approximator.predict(next_state, a) - self._alpha_np * log_prob_next
q *= 1 - absorbing
return q
@property
def _alpha(self):
return self._log_alpha.exp()
@property
def _alpha_np(self):
return self._alpha.detach().cpu().numpy()