def learn(alg, alg_params):
mdp = LQR.generate(dimensions=1)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
approximator_params = dict(input_dim=mdp.info.observation_space.shape)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape,
params=approximator_params)
sigma = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape,
params=approximator_params)
sigma_weights = 2 * np.ones(sigma.weights_size)
sigma.set_weights(sigma_weights)
policy = StateStdGaussianPolicy(approximator, sigma)
agent = alg(policy, mdp.info, **alg_params)
core = Core(agent, mdp)
core.learn(n_episodes=10, n_episodes_per_fit=5)
return policy
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 __init__(self,
policy,
mu,
mdp_info,
alpha_theta,
alpha_omega,
alpha_v,
value_function_features=None,
policy_features=None):
self._mu = mu
self._psi = value_function_features
self._alpha_theta = alpha_theta
self._alpha_omega = alpha_omega
self._alpha_v = alpha_v
if self._psi is not None:
input_shape = (self._psi.size, )
else:
input_shape = mdp_info.observation_space.shape
self._V = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=(1, ))
self._A = Regressor(LinearApproximator,
input_shape=(self._mu.weights_size, ),
output_shape=(1, ))
super().__init__(policy, mdp_info, policy_features)
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)
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)
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 __init__(self, policy, mdp_info, alpha_theta, alpha_v, lambda_par=.9,
value_function_features=None, policy_features=None):
"""
Constructor.
Args:
policy (ParametricPolicy): a differentiable stochastic policy;
mdp_info: information about the MDP;
alpha_theta (Parameter): learning rate for policy update;
alpha_v (Parameter): learning rate for the value function;
lambda_par (float, 0.9): trace decay parameter;
value_function_features (Features, None): features used by the value
function approximator;
policy_features (Features, None): features used by the policy.
"""
self._psi = value_function_features
self._alpha_theta = alpha_theta
self._alpha_v = alpha_v
self._lambda = lambda_par
super().__init__(policy, mdp_info, policy_features)
if self._psi is not None:
input_shape = (self._psi.size,)
else:
input_shape = mdp_info.observation_space.shape
self._V = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=(1,))
self._e_v = np.zeros(self._V.weights_size)
self._e_theta = np.zeros(self.policy.weights_size)
def __init__(self,
approximator,
policy,
mdp_info,
fit_params=None,
approximator_params=None,
features=None):
"""
Constructor.
Args:
approximator (object): approximator used by the algorithm and the
policy.
fit_params (dict, None): parameters of the fitting algorithm of the
approximator;
approximator_params (dict, None): parameters of the approximator to
build;
"""
self._fit_params = dict() if fit_params is None else fit_params
self._approximator_params = dict() if approximator_params is None else\
approximator_params
self.approximator = Regressor(approximator,
**self._approximator_params)
policy.set_q(self.approximator)
super().__init__(policy, mdp_info, features)
def test_ornstein_uhlenbeck_policy():
np.random.seed(88)
mu = Regressor(LinearApproximator, input_shape=(5, ), output_shape=(2, ))
pi = OrnsteinUhlenbeckPolicy(mu, sigma=np.ones(1) * .2, theta=.15, dt=1e-2)
w = np.random.randn(pi.weights_size)
pi.set_weights(w)
assert np.array_equal(pi.get_weights(), w)
state = np.random.randn(5)
action = pi.draw_action(state)
action_test = np.array([-1.95896171, 1.91292747])
assert np.allclose(action, action_test)
pi.reset()
action = pi.draw_action(state)
action_test = np.array([-1.94161061, 1.92233358])
assert np.allclose(action, action_test)
try:
pi(state, action)
except NotImplementedError:
pass
else:
assert False
def __init__(self,
approximator,
policy,
mdp_info,
n_iterations,
fit_params=None,
approximator_params=None,
features=None,
quiet=False):
"""
Constructor.
Args:
approximator (object): approximator used by the algorithm and the
policy.
n_iterations (int): number of iterations to perform for training;
fit_params (dict, None): parameters of the fitting algorithm of the
approximator;
approximator_params (dict, None): parameters of the approximator to
build;
quiet (bool, False): whether to show the progress bar or not.
"""
self._n_iterations = n_iterations
self._fit_params = dict() if fit_params is None else fit_params
self._approximator_params = dict() if approximator_params is None else\
approximator_params
self._quiet = quiet
self.approximator = Regressor(approximator,
**self._approximator_params)
policy.set_q(self.approximator)
super(BatchTD, self).__init__(policy, mdp_info, features)
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)
def __init__(self,
policy,
mdp_info,
alpha_theta,
alpha_v,
alpha_r,
lambda_par=.9,
value_function_features=None,
policy_features=None):
"""
Constructor.
Args:
policy (ParametricPolicy): a differentiable stochastic policy;
mdp_info: information about the MDP;
alpha_theta (Parameter): learning rate for policy update;
alpha_v (Parameter): learning rate for the value function;
alpha_r (Parameter): learning rate for the reward trace;
lambda_par (float, 0.9): trace decay parameter;
value_function_features (Features, None): features used by the
value function approximator;
policy_features (Features, None): features used by the policy.
"""
self._psi = value_function_features
self._alpha_theta = alpha_theta
self._alpha_v = alpha_v
self._alpha_r = alpha_r
self._lambda = lambda_par
super().__init__(policy, mdp_info, policy_features)
if self._psi is not None:
input_shape = (self._psi.size, )
else:
input_shape = mdp_info.observation_space.shape
self._V = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=(1, ))
self._e_v = np.zeros(self._V.weights_size)
self._e_theta = np.zeros(self.policy.weights_size)
self._r_bar = 0
def __init__(self,
policy,
mu,
mdp_info,
alpha_theta,
alpha_omega,
alpha_v,
value_function_features=None,
policy_features=None):
"""
Constructor.
Args:
policy (Policy): any exploration policy, possibly using the deterministic
policy as mean regressor;
mu (Regressor): regressor that describe the deterministic policy to be
learned i.e., the deterministic mapping between state and action.
alpha_theta (Parameter): learning rate for policy update;
alpha_omega (Parameter): learning rate for the advantage function;
alpha_v (Parameter): learning rate for the value function;
value_function_features (Features, None): features used by the value
function approximator;
policy_features (Features, None): features used by the policy.
"""
self._mu = mu
self._psi = value_function_features
self._alpha_theta = alpha_theta
self._alpha_omega = alpha_omega
self._alpha_v = alpha_v
if self._psi is not None:
input_shape = (self._psi.size, )
else:
input_shape = mdp_info.observation_space.shape
self._V = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=(1, ))
self._A = Regressor(LinearApproximator,
input_shape=(self._mu.weights_size, ),
output_shape=(1, ))
super().__init__(policy, mdp_info, policy_features)
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 __init__(self, mdp_info, policy, critic_params, actor_optimizer,
n_epochs_policy, batch_size, eps_ppo, lam, quiet=True,
critic_fit_params=None):
self._critic_fit_params = dict(n_epochs=10) if critic_fit_params is None else critic_fit_params
self._n_epochs_policy = n_epochs_policy
self._batch_size = batch_size
self._eps_ppo = eps_ppo
self._optimizer = actor_optimizer['class'](policy.parameters(), **actor_optimizer['params'])
self._lambda = lam
self._V = Regressor(TorchApproximator, **critic_params)
self._quiet = quiet
self._iter = 1
super().__init__(policy, mdp_info, None)
def experiment(n_epochs, n_episodes):
np.random.seed()
# MDP
n_steps = 5000
mdp = InvertedPendulum(horizon=n_steps)
# Agent
n_tilings = 10
alpha_theta = Parameter(5e-3 / n_tilings)
alpha_omega = Parameter(0.5 / n_tilings)
alpha_v = Parameter(0.5 / n_tilings)
tilings = Tiles.generate(n_tilings, [10, 10],
mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
phi = Features(tilings=tilings)
input_shape = (phi.size,)
mu = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
sigma = 1e-1 * np.eye(1)
policy = GaussianPolicy(mu, sigma)
agent = COPDAC_Q(policy, mu, mdp.info,
alpha_theta, alpha_omega, alpha_v,
value_function_features=phi,
policy_features=phi)
# Train
dataset_callback = CollectDataset()
visualization_callback = Display(agent._V, mu,
mdp.info.observation_space.low,
mdp.info.observation_space.high,
phi, phi)
core = Core(agent, mdp, callbacks=[dataset_callback])
for i in range(n_epochs):
core.learn(n_episodes=n_episodes,
n_steps_per_fit=1, render=False)
J = compute_J(dataset_callback.get(), gamma=1.0)
dataset_callback.clean()
visualization_callback()
print('Mean Reward at iteration ' + str(i) + ': ' +
str(np.sum(J) / n_steps / n_episodes))
print('Press a button to visualize the pendulum...')
input()
sigma = 1e-8 * np.eye(1)
policy.set_sigma(sigma)
core.evaluate(n_steps=n_steps, render=True)
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)
def __init__(self,
mdp_info,
policy,
critic_params,
actor_optimizer,
ent_coeff,
max_grad_norm=None,
critic_fit_params=None):
"""
Constructor.
Args:
policy (TorchPolicy): torch policy to be learned by the algorithm
critic_params (dict): parameters of the critic approximator to
build;
actor_optimizer (dict): parameters to specify the actor optimizer
algorithm;
ent_coeff (float, 0): coefficient for the entropy penalty;
max_grad_norm (float, None): maximum norm for gradient clipping.
If None, no clipping will be performed, unless specified
otherwise in actor_optimizer;
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._entropy_coeff = ent_coeff
self._V = Regressor(TorchApproximator, **critic_params)
if 'clipping' not in actor_optimizer and max_grad_norm is not None:
actor_optimizer = deepcopy(actor_optimizer)
clipping_params = dict(max_norm=max_grad_norm, norm_type=2)
actor_optimizer['clipping'] = dict(
method=torch.nn.utils.clip_grad_norm_, params=clipping_params)
super().__init__(policy, mdp_info, actor_optimizer,
policy.parameters())
def __init__(self, policy, mu, mdp_info, alpha_theta, alpha_omega, alpha_v,
value_function_features=None, policy_features=None):
self._mu = mu
self._psi = value_function_features
self._alpha_theta = alpha_theta
self._alpha_omega = alpha_omega
self._alpha_v = alpha_v
if self._psi is not None:
input_shape = (self._psi.size,)
else:
input_shape = mdp_info.observation_space.shape
self._V = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=(1,))
self._A = Regressor(LinearApproximator,
input_shape=(self._mu.weights_size,),
output_shape=(1,))
super().__init__(policy, mdp_info, policy_features)
def __init__(self, mdp_info, policy, critic_params, actor_optimizer,
n_epochs_policy, batch_size, eps_ppo, lam, quiet=True,
critic_fit_params=None):
"""
Constructor.
Args:
policy (TorchPolicy): torch policy to be learned by the algorithm
critic_params (dict): parameters of the critic approximator to
build;
actor_optimizer (dict): parameters to specify the actor optimizer
algorithm;
n_epochs_policy (int): number of policy updates for every dataset;
batch_size (int): size of minibatches for every optimization step
eps_ppo (float): value for probability ratio clipping;
lam float(float, 1.): lambda coefficient used by generalized
advantage estimation;
quiet (bool, True): if true, the algorithm will print debug
information;
critic_fit_params (dict, None): parameters of the fitting algorithm
of the critic approximator.
"""
self._critic_fit_params = dict(n_epochs=10) if critic_fit_params is None else critic_fit_params
self._n_epochs_policy = n_epochs_policy
self._batch_size = batch_size
self._eps_ppo = eps_ppo
self._optimizer = actor_optimizer['class'](policy.parameters(), **actor_optimizer['params'])
self._lambda = lam
self._V = Regressor(TorchApproximator, **critic_params)
self._quiet = quiet
self._iter = 1
super().__init__(policy, mdp_info, None)
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 __init__(self, approximator, policy, mdp_info, params, features=None):
"""
Constructor.
Args:
approximator (object): approximator used by the algorithm and the
policy.
"""
self._n_iterations = params['algorithm_params']['n_iterations']
self._quiet = params['algorithm_params'].get('quiet', False)
self.approximator = Regressor(approximator,
**params['approximator_params'])
policy.set_q(self.approximator)
super(BatchTD, self).__init__(policy, mdp_info, params, features)
def experiment(n_epochs, n_steps, n_eval_episodes):
np.random.seed()
# MDP
mdp = InvertedPendulum()
# Agent
n_tilings = 10
alpha_theta = ExponentialDecayParameter(1, decay_exp=1.0)
alpha_omega = ExponentialDecayParameter(1.5 / n_tilings, decay_exp=2 / 3)
alpha_v = ExponentialDecayParameter(1 / n_tilings, decay_exp=2 / 3)
tilings = Tiles.generate(n_tilings, [10, 10],
mdp.info.observation_space.low,
mdp.info.observation_space.high)
phi = Features(tilings=tilings)
input_shape = (phi.size, )
mu = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
sigma = 1e-3 * np.eye(1)
policy = GaussianPolicy(mu, sigma)
agent = COPDAC_Q(policy,
mu,
mdp.info,
alpha_theta,
alpha_omega,
alpha_v,
value_function_features=phi,
policy_features=phi)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=n_eval_episodes)
J = compute_J(dataset_eval, gamma=1.0)
print('Total Reward per episode at start : ' + str(np.mean(J)))
for i in range(n_epochs):
core.learn(n_steps=n_steps, n_steps_per_fit=1)
dataset_eval = core.evaluate(n_episodes=n_eval_episodes, render=False)
J = compute_J(dataset_eval, gamma=1.0)
print('Total Reward per episode at iteration ' + str(i) + ': ' +
str(np.mean(J)))
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()
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 experiment(n_epochs, ep_per_epoch_train, ep_per_epoch_eval, n_iterations):
np.random.seed()
# MDP
mdp = PreyPredator()
basis = PolynomialBasis.generate(1, mdp.info.observation_space.shape[0])
phi = Features(basis_list=basis[1:])
# Features
approximator = Regressor(LinearApproximator,
input_shape=(phi.size, ),
output_shape=mdp.info.action_space.shape)
sigma = 1e-2 * np.eye(mdp.info.action_space.shape[0])
policy = GaussianPolicy(approximator, sigma)
lr = Parameter(1e-5)
#agent = GPOMDP(policy, mdp.info, lr, phi)
agent = KeyboardAgent()
# Train
core = Core(agent, mdp)
dataset = core.evaluate(n_episodes=ep_per_epoch_eval, render=True)
J = compute_J(dataset, gamma=mdp.info.gamma)
print('Reward at start: ', np.mean(J))
for i in range(n_epochs):
core.learn(n_episodes=ep_per_epoch_train,
n_episodes_per_fit=ep_per_epoch_train // n_iterations,
render=False)
dataset = core.evaluate(n_episodes=ep_per_epoch_eval, render=True)
J = compute_J(dataset, gamma=mdp.info.gamma)
p = policy.get_weights()
print('mu: ', p)
print('Reward at iteration ', i, ': ', np.mean(J))
print('Press a button to visualize the segway...')
input()
core.evaluate(n_episodes=3, render=True)
def experiment(alg, params, n_epochs, n_episodes, n_ep_per_fit):
np.random.seed()
# MDP
mdp = Segway()
# Policy
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
n_weights = approximator.weights_size
mu = np.zeros(n_weights)
sigma = 2e-0 * np.ones(n_weights)
policy = DeterministicPolicy(approximator)
dist = GaussianDiagonalDistribution(mu, sigma)
agent = alg(dist, policy, mdp.info, **params)
# Train
print(alg.__name__)
dataset_callback = CollectDataset()
core = Core(agent, mdp, callbacks=[dataset_callback])
for i in range(n_epochs):
core.learn(n_episodes=n_episodes,
n_episodes_per_fit=n_ep_per_fit,
render=False)
J = compute_J(dataset_callback.get(), gamma=mdp.info.gamma)
dataset_callback.clean()
p = dist.get_parameters()
print('mu: ', p[:n_weights])
print('sigma: ', p[n_weights:])
print('Reward at iteration ' + str(i) + ': ' + str(np.mean(J)))
print('Press a button to visualize the segway...')
input()
core.evaluate(n_episodes=3, render=True)
def test_copdac_q():
n_steps = 50
mdp = InvertedPendulum(horizon=n_steps)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
# Agent
n_tilings = 1
alpha_theta = Parameter(5e-3 / n_tilings)
alpha_omega = Parameter(0.5 / n_tilings)
alpha_v = Parameter(0.5 / n_tilings)
tilings = Tiles.generate(n_tilings, [2, 2],
mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
phi = Features(tilings=tilings)
input_shape = (phi.size,)
mu = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
sigma = 1e-1 * np.eye(1)
policy = GaussianPolicy(mu, sigma)
agent = COPDAC_Q(policy, mu, mdp.info,
alpha_theta, alpha_omega, alpha_v,
value_function_features=phi,
policy_features=phi)
# Train
core = Core(agent, mdp)
core.learn(n_episodes=2, n_episodes_per_fit=1)
w = agent.policy.get_weights()
w_test = np.array([0, -6.62180045e-7, 0, -4.23972882e-2])
assert np.allclose(w, w_test)
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)
def learn(alg, **alg_params):
np.random.seed(1)
torch.manual_seed(1)
# MDP
mdp = LQR.generate(dimensions=2)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(mu=approximator)
mu = np.zeros(policy.weights_size)
sigma = 1e-3 * np.ones(policy.weights_size)
distribution = GaussianDiagonalDistribution(mu, sigma)
agent_test = alg(distribution, policy, mdp.info, **alg_params)
core = Core(agent_test, mdp)
core.learn(n_episodes=5, n_episodes_per_fit=5)
return distribution
mdp = ShipSteering()
high = [150, 150, np.pi]
low = [0, 0, -np.pi]
n_tiles = [5, 5, 6]
low = np.array(low, dtype=np.float)
high = np.array(high, dtype=np.float)
n_tilings = 1
tilings = Tiles.generate(n_tilings=n_tilings, n_tiles=n_tiles, low=low,
high=high)
phi = Features(tilings=tilings)
input_shape = (phi.size,)
approximator = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(approximator)
mu = np.zeros(policy.weights_size)
sigma = 4e-1 * np.ones(policy.weights_size)
distribution_test = GaussianDiagonalDistribution(mu, sigma)
agent_test = RWR(distribution_test, policy, mdp.info, beta=1.)
core = Core(agent_test, mdp)
s = np.arange(10)
a = np.arange(10)
r = np.arange(10)
ss = s + 5
ab = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
last = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
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()
def experiment(n_epochs, n_episodes):
np.random.seed()
# MDP
n_steps = 5000
mdp = InvertedPendulum(horizon=n_steps)
# Agent
n_tilings = 11
alpha_r = Parameter(.0001)
alpha_theta = Parameter(.001 / n_tilings)
alpha_v = Parameter(.1 / n_tilings)
tilings = Tiles.generate(n_tilings-1, [10, 10],
mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
phi = Features(tilings=tilings)
tilings_v = tilings + Tiles.generate(1, [1, 1],
mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
psi = Features(tilings=tilings_v)
input_shape = (phi.size,)
mu = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
std = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
std_0 = np.sqrt(1.)
std.set_weights(np.log(std_0) / n_tilings * np.ones(std.weights_size))
policy = StateLogStdGaussianPolicy(mu, std)
agent = SAC_AVG(policy, mdp.info,
alpha_theta, alpha_v, alpha_r,
lambda_par=.5,
value_function_features=psi,
policy_features=phi)
# Train
dataset_callback = CollectDataset()
display_callback = Display(agent._V, mu, std,
mdp.info.observation_space.low,
mdp.info.observation_space.high,
phi, psi)
core = Core(agent, mdp, callbacks=[dataset_callback])
for i in range(n_epochs):
core.learn(n_episodes=n_episodes,
n_steps_per_fit=1, render=False)
J = compute_J(dataset_callback.get(), gamma=1.)
dataset_callback.clean()
display_callback()
print('Mean Reward at iteration ' + str(i) + ': ' +
str(np.sum(J) / n_steps/n_episodes))
print('Press a button to visualize the pendulum...')
input()
core.evaluate(n_steps=n_steps, render=True)
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
class SAC_AVG(Agent):
"""
Stochastic Actor critic in the average reward setting as presented in:
"Model-Free Reinforcement Learning with Continuous Action in Practice".
Degris T. et al.. 2012.
"""
def __init__(self, policy, mdp_info, alpha_theta, alpha_v, alpha_r,
lambda_par=.9, value_function_features=None,
policy_features=None):
"""
Constructor.
Args:
policy (ParametricPolicy): a differentiable stochastic policy;
mdp_info: information about the MDP;
alpha_theta (Parameter): learning rate for policy update;
alpha_v (Parameter): learning rate for the value function;
alpha_r (Parameter): learning rate for the reward trace;
lambda_par (float, 0.9): trace decay parameter;
value_function_features (Features, None): features used by the
value function approximator;
policy_features (Features, None): features used by the policy.
"""
self._psi = value_function_features
self._alpha_theta = alpha_theta
self._alpha_v = alpha_v
self._alpha_r = alpha_r
self._lambda = lambda_par
super().__init__(policy, mdp_info, policy_features)
if self._psi is not None:
input_shape = (self._psi.size,)
else:
input_shape = mdp_info.observation_space.shape
self._V = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=(1,))
self._e_v = np.zeros(self._V.weights_size)
self._e_theta = np.zeros(self.policy.weights_size)
self._r_bar = 0
def episode_start(self):
self._e_v = np.zeros(self._V.weights_size)
self._e_theta = np.zeros(self.policy.weights_size)
def fit(self, dataset):
for step in dataset:
s, a, r, ss, absorbing, _ = step
s_phi = self.phi(s) if self.phi is not None else s
s_psi = self._psi(s) if self._psi is not None else s
ss_psi = self._psi(ss) if self._psi is not None else ss
v_next = self._V(ss_psi) if not absorbing else 0
# Compute TD error
delta = r - self._r_bar + v_next - self._V(s_psi)
# Update traces
self._r_bar += self._alpha_r() * delta
self._e_v = self._lambda * self._e_v + s_psi
self._e_theta = self._lambda * self._e_theta + \
self.policy.diff_log(s_phi, a)
# Update value function
delta_v = self._alpha_v(s, a) * delta * self._e_v
v_new = self._V.get_weights() + delta_v
self._V.set_weights(v_new)
# Update policy
delta_theta = self._alpha_theta(s, a) * delta * self._e_theta
theta_new = self.policy.get_weights() + delta_theta
self.policy.set_weights(theta_new)
class COPDAC_Q(Agent):
def __init__(self, policy, mu, mdp_info, alpha_theta, alpha_omega, alpha_v,
value_function_features=None, policy_features=None):
self._mu = mu
self._psi = value_function_features
self._alpha_theta = alpha_theta
self._alpha_omega = alpha_omega
self._alpha_v = alpha_v
if self._psi is not None:
input_shape = (self._psi.size,)
else:
input_shape = mdp_info.observation_space.shape
self._V = Regressor(LinearApproximator, input_shape=input_shape,
output_shape=(1,))
self._A = Regressor(LinearApproximator,
input_shape=(self._mu.weights_size,),
output_shape=(1,))
super().__init__(policy, mdp_info, policy_features)
def fit(self, dataset):
for step in dataset:
s, a, r, ss, absorbing, _ = step
s_phi = self.phi(s) if self.phi is not None else s
s_psi = self._psi(s) if self._psi is not None else s
ss_psi = self._psi(ss) if self._psi is not None else ss
q_next = np.asscalar(self._V(ss_psi)) if not absorbing else 0
grad_mu_s = np.atleast_2d(self._mu.diff(s_phi))
omega = self._A.get_weights()
delta = r + self.mdp_info.gamma * q_next - self._Q(s, a)
delta_theta = self._alpha_theta(s, a) * \
omega.dot(grad_mu_s.T).dot(grad_mu_s)
delta_omega = self._alpha_omega(s, a) * delta * self._nu(s, a)
delta_v = self._alpha_v(s, a) * delta * s_psi
theta_new = self._mu.get_weights() + delta_theta
self._mu.set_weights(theta_new)
omega_new = omega + delta_omega
self._A.set_weights(omega_new)
v_new = self._V.get_weights() + delta_v
self._V.set_weights(v_new)
def _Q(self, state, action):
state_psi = self._psi(state) if self._psi is not None else state
return np.asscalar(self._V(state_psi)) + \
np.asscalar(self._A(self._nu(state, action)))
def _nu(self, state, action):
state_phi = self.phi(state) if self.phi is not None else state
grad_mu = np.atleast_2d(self._mu.diff(state_phi))
delta = action - self._mu(state_phi)
return delta.dot(grad_mu)
def learn(alg):
n_steps = 50
mdp = InvertedPendulum(horizon=n_steps)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
# Agent
n_tilings = 2
alpha_r = Parameter(.0001)
alpha_theta = Parameter(.001 / n_tilings)
alpha_v = Parameter(.1 / n_tilings)
tilings = Tiles.generate(n_tilings - 1, [1, 1],
mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
phi = Features(tilings=tilings)
tilings_v = tilings + Tiles.generate(
1, [1, 1], mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
psi = Features(tilings=tilings_v)
input_shape = (phi.size, )
mu = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
std = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
std_0 = np.sqrt(1.)
std.set_weights(np.log(std_0) / n_tilings * np.ones(std.weights_size))
policy = StateLogStdGaussianPolicy(mu, std)
if alg is StochasticAC:
agent = alg(policy,
mdp.info,
alpha_theta,
alpha_v,
lambda_par=.5,
value_function_features=psi,
policy_features=phi)
elif alg is StochasticAC_AVG:
agent = alg(policy,
mdp.info,
alpha_theta,
alpha_v,
alpha_r,
lambda_par=.5,
value_function_features=psi,
policy_features=phi)
core = Core(agent, mdp)
core.learn(n_episodes=2, n_episodes_per_fit=1)
return policy
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()
