I'm using MushroomRL for a Deep Reinforcement Learning project, and I'm using a Graph representation as RL Environment where the number of nodes represents the number of actions now in my neural network the input is one value ex: tensor([[5.]], and the output Q is the number of nodes which is ten ex: tensor([[5972.4927, 8562.3330, 7443.6479, 7326.1587, 6615.2090, 6617.3145,6911.8672, 8233.7930, 6821.0093, 7000.1182,]] now I'm using a new framework called MushroomRL, and this is the code
if __name__ == '__main__':
from mushroom_rl.core import Core
from mushroom_rl.algorithms.value import TrueOnlineSARSALambda
from mushroom_rl.policy import EpsGreedy
from mushroom_rl.features import Features
from mushroom_rl.features.tiles import Tiles
from mushroom_rl.utils.dataset import compute_J
from mushroom_rl.utils.parameters import LinearParameter, Parameter
from mushroom_rl.approximators.parametric import TorchApproximator
from mushroom_rl.algorithms.value import DQN
# Set the seed
np.random.seed(1)
# Create the toy environment with default parameters
#mdp = Environment.make('graph_env')
mdp=graph_env()
# Using an epsilon-greedy policy
epsilon = Parameter(value=0.1)
pi = EpsGreedy(epsilon=epsilon)
# Policy
epsilon = LinearParameter(value=1.,
threshold_value=.1,
n=1000000)
epsilon_test = Parameter(value=.05)
epsilon_random = Parameter(value=1)
pi = EpsGreedy(epsilon=epsilon_random)
approximator_params = dict(
network=Network,
input_shape=(1,),
output_shape=(1,),
n_actions=mdp.info.action_space.n,
optimizer=optimizer,
loss=F.mse_loss
)
approximator = TorchApproximator
algorithm_params = dict(
batch_size=32,
target_update_frequency=target_update_frequency // train_frequency,
replay_memory=True,
initial_replay_size=initial_replay_size,
max_replay_size=max_replay_size
)
agent=agent = DQN(mdp.info, pi, approximator,
approximator_params=approximator_params,
**algorithm_params)
# Algorithm
core = Core(agent, mdp)
# RUN
# Fill replay memory with random dataset
print_epoch(0)
core.learn(n_steps=initial_replay_size,n_steps_per_fit=initial_replay_size)
# Evaluate initial policy
pi.set_epsilon(epsilon_test)
#mdp.set_episode_end(False)
dataset = core.evaluate(n_steps=test_samples)
scores.append(get_stats(dataset))
for n_epoch in range(1, max_steps // evaluation_frequency + 1):
print_epoch(n_epoch)
print('- Learning:')
# learning step
pi.set_epsilon(epsilon)
mdp.set_episode_end(True)
core.learn(n_steps=evaluation_frequency,
n_steps_per_fit=train_frequency)
print('- Evaluation:')
# evaluation step
pi.set_epsilon(epsilon_test)
mdp.set_episode_end(False)
dataset = core.evaluate(n_steps=test_samples)
scores.append(get_stats(dataset))
it givs me this error when i run the code
TypeError: empty() received an invalid combination of arguments - got (tuple, dtype=NoneType, device=NoneType), but expected one of:
* (tuple of ints size, *, tuple of names names, torch.memory_format memory_format, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
* (tuple of ints size, *, torch.memory_format memory_format, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
i beleve the proplem in part of the code can any one help to fix it ?
I am trying to implement a multitask neural network used by a paper but am quite unsure how I should code the multitask network because the authors did not provide code for that part.
The network architecture looks like (paper):
To make it simpler, the network architecture could be generalized as (For demo I changed their more complicated operation for the pair of individual embeddings to concatenation):
The authors are summing the loss from the individual tasks and the pairwise tasks, and using the total loss to optimize the parameters for the three networks (encoder, MLP-1, MLP-2) in each batch, but I am kind of at sea as to how different types of data are combined in a single batch to feed into two different networks that share an initial encoder. I tried to search for other networks with similar structure but did not find any sources. Would appreciate any thoughts!
This is actually a common pattern. It would be solved by code like the following.
class Network(nn.Module):
def __init__(self, ...):
self.encoder = DrugTargetInteractiongNetwork()
self.mlp1 = ClassificationMLP()
self.mlp2 = PairwiseMLP()
def forward(self, data_a, data_b):
a_encoded = self.encoder(data_a)
b_encoded = self.encoder(data_b)
a_classified = self.mlp1(a_encoded)
b_classified = self.mlp1(b_encoded)
# let me assume data_a and data_b are of shape
# [batch_size, n_molecules, n_features].
# and that those n_molecules are not necessarily
# equal.
# This can be generalized to more dimensions.
a_broadcast, b_broadcast = torch.broadcast_tensors(
a_encoded[:, None, :, :],
b_encoded[:, :, None, :],
)
# this will work if your mlp2 accepts an arbitrary number of
# learding dimensions and just broadcasts over them. That's true
# for example if it uses just Linear and pointwise
# operations, but may fail if it makes some specific assumptions
# about the number of dimensions of the inputs
pairwise_classified = self.mlp2(a_broadcast, b_broadcast)
# if that is a problem, you have to reshape it such that it
# works. Most torch models accept at least a leading batch dimension
# for vectorization, so we can "fold" the pairwise dimension
# into the batch dimension, presenting it as
# [batch*n_mol_1*n_mol_2, n_features]
# to mlp2 and then recover it back
B, N1, N_feat = a_broadcast.shape
_B, N2, _N_feat = b_broadcast.shape
a_batched = a_broadcast.reshape(B*N1*N2, N_feat)
b_batched = b_broadcast.reshape(B*N1*N2, N_feat)
# above, -1 would suffice instead of B*N1*N2, just being explicit
batch_output = self.mlp2(a_batched, b_batched)
# this should be exactly the same as `pairwise_classified`
alternative_classified = batch_output.reshape(B, N1, N2, -1)
return a_classified, b_classified, pairwise_classified
I just find that in the code here:
https://github.com/NUS-Tim/Pytorch-WGAN/tree/master/models
The "generator" loss, G, between WGAN and WGAN-GP is different, for WGAN:
g_loss = self.D(fake_images)
g_loss = g_loss.mean().mean(0).view(1)
g_loss.backward(one) # !!!
g_cost = -g_loss
But for WGAN-GP:
g_loss = self.D(fake_images)
g_loss = g_loss.mean()
g_loss.backward(mone) # !!!
g_cost = -g_loss
Why one is one=1 and another is mone=-1?
You might have misread the source code, the first sample you gave is not averaging the resut of D to compute its loss but instead uses the binary cross-entropy.
To be more precise:
The first method ("GAN") uses the BCE loss to compute the loss terms for D and G. The standard GAN optimization objective for D is to minimize E_x[log(D(x))] + E_z[log(1-D(G(z)))]. Source code:
outputs = self.D(images)
d_loss_real = self.loss(outputs.flatten(), real_labels) # <- bce loss
real_score = outputs
# Compute BCELoss using fake images
fake_images = self.G(z)
outputs = self.D(fake_images)
d_loss_fake = self.loss(outputs.flatten(), fake_labels) # <- bce loss
fake_score = outputs
# Optimizie discriminator
d_loss = d_loss_real + d_loss_fake
self.D.zero_grad()
d_loss.backward()
self.d_optimizer.step()
For d_loss_real you optimize towards 1s (output is considered real), while d_loss_fake optimizes towards 0s (output is considered fake).
While the second ("WCGAN") uses the Wasserstein loss (ref) whereby we maximise for D the loss: E_x[D(x)] - E_z[D(G(z))]. Source code:
# Train discriminator
# WGAN - Training discriminator more iterations than generator
# Train with real images
d_loss_real = self.D(images)
d_loss_real = d_loss_real.mean()
d_loss_real.backward(mone)
# Train with fake images
z = self.get_torch_variable(torch.randn(self.batch_size, 100, 1, 1))
fake_images = self.G(z)
d_loss_fake = self.D(fake_images)
d_loss_fake = d_loss_fake.mean()
d_loss_fake.backward(one)
# [...]
Wasserstein_D = d_loss_real - d_loss_fake
By doing d_loss_real.backward(mone) you backpropage with a gradient of opposite sign, i.e. its's a gradient ascend, and you end up maximizing d_loss_real.
In order to Update D network:
lossD = Expectation of D(fake data) - Expectation of D(real data) + gradient penalty
lossD ↓，D(real data) ↑
so you need to add minus one to the gradient process
I would like to estimate the parameters of a mixture model of normal distributions in OpenTURNS (that is, the distribution of a weighted sum of Gaussian random variables). OpenTURNS can create such a mixture, but it cannot estimate its parameters. Moreover, I need to create the mixture as an OpenTURNS distribution in order to propagate uncertainty through a function.
For example, I know how to create a mixture of two normal distributions:
import openturns as ot
mu1 = 1.0
sigma1 = 0.5
mu2 = 3.0
sigma2 = 2.0
weights = [0.3, 0.7]
n1 = ot.Normal(mu1, sigma1)
n2 = ot.Normal(mu2, sigma2)
m = ot.Mixture([n1, n2], weights)
In this example, I would like to estimate mu1, sigma1, mu2, sigma2 on a given sample. In order to create a working example, it is easy to generate a sample by simulation.
s = m.getSample(100)
You can rely on scikit-learn's GaussianMixture to estimate the parameters and then use them to define a Mixture model in OpenTURNS.
The script hereafter contains a Python class MixtureFactory that estimates the parameters of a scikitlearn GaussianMixture and outputs an OpenTURNS Mixture distribution:
from sklearn.mixture import GaussianMixture
from sklearn.utils.validation import check_is_fitted
import openturns as ot
import numpy as np
class MixtureFactory(GaussianMixture):
"""
Representation of a Gaussian mixture model probability distribution.
This class allows to estimate the parameters of a Gaussian mixture
distribution using scikit algorithms & provides openturns Mixture object.
Read more in scikit learn user guide & openturns theory.
Parameters:
-----------
n_components : int, defaults to 1.
The number of mixture components.
covariance_type : {'full' (default), 'tied', 'diag', 'spherical'}
String describing the type of covariance parameters to use.
Must be one of:
'full'
each component has its own general covariance matrix
'tied'
all components share the same general covariance matrix
'diag'
each component has its own diagonal covariance matrix
'spherical'
each component has its own single variance
tol : float, defaults to 1e-3.
The convergence threshold. EM iterations will stop when the
lower bound average gain is below this threshold.
reg_covar : float, defaults to 1e-6.
Non-negative regularization added to the diagonal of covariance.
Allows to assure that the covariance matrices are all positive.
max_iter : int, defaults to 100.
The number of EM iterations to perform.
n_init : int, defaults to 1.
The number of initializations to perform. The best results are kept.
init_params : {'kmeans', 'random'}, defaults to 'kmeans'.
The method used to initialize the weights, the means and the
precisions.
Must be one of::
'kmeans' : responsibilities are initialized using kmeans.
'random' : responsibilities are initialized randomly.
weights_init : array-like, shape (n_components, ), optional
The user-provided initial weights, defaults to None.
If it None, weights are initialized using the `init_params` method.
means_init : array-like, shape (n_components, n_features), optional
The user-provided initial means, defaults to None,
If it None, means are initialized using the `init_params` method.
precisions_init : array-like, optional.
The user-provided initial precisions (inverse of the covariance
matrices), defaults to None.
If it None, precisions are initialized using the 'init_params' method.
The shape depends on 'covariance_type'::
(n_components,) if 'spherical',
(n_features, n_features) if 'tied',
(n_components, n_features) if 'diag',
(n_components, n_features, n_features) if 'full'
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
warm_start : bool, default to False.
If 'warm_start' is True, the solution of the last fitting is used as
initialization for the next call of fit(). This can speed up
convergence when fit is called several times on similar problems.
In that case, 'n_init' is ignored and only a single initialization
occurs upon the first call.
See :term:`the Glossary <warm_start>`.
verbose : int, default to 0.
Enable verbose output. If 1 then it prints the current
initialization and each iteration step. If greater than 1 then
it prints also the log probability and the time needed
for each step.
verbose_interval : int, default to 10.
Number of iteration done before the next print.
"""
def __init__(self, n_components=2, covariance_type='full', tol=1e-6,
reg_covar=1e-6, max_iter=1000, n_init=1, init_params='kmeans',
weights_init=None, means_init=None, precisions_init=None,
random_state=41, warm_start=False,
verbose=0, verbose_interval=10):
super().__init__(n_components, covariance_type, tol, reg_covar,
max_iter, n_init, init_params, weights_init, means_init,
precisions_init, random_state, warm_start, verbose, verbose_interval)
def fit(self, X):
"""
Fit the mixture model parameters.
EM algorithm is applied here to estimate the model parameters and build a
Mixture distribution (see openturns mixture).
The method fits the model ``n_init`` times and sets the parameters with
which the model has the largest likelihood or lower bound. Within each
trial, the method iterates between E-step and M-step for ``max_iter``
times until the change of likelihood or lower bound is less than
``tol``, otherwise, a ``ConvergenceWarning`` is raised.
If ``warm_start`` is ``True``, then ``n_init`` is ignored and a single
initialization is performed upon the first call. Upon consecutive
calls, training starts where it left off.
Parameters
----------
X : array-like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
"""
data = np.array(X)
# Evaluate the model parameters.
super().fit(data)
# openturns mixture
# n_components ==> weight of size n_components
weights = self.weights_
n_components = len(weights)
# Create ot distribution
collection = n_components * [0]
# Covariance matrices
cov = self.covariances_
mu = self.means_
# means : n_components x n_features
n_components, n_features = mu.shape
# Following the type of covariance, we define the collection of gaussians
# Spherical : C_k = Identity * sigma_k
if self.covariance_type is 'spherical':
c = ot.CorrelationMatrix(n_features)
for l in range(n_components):
sigma = np.sqrt(cov[l])
collection[l] = ot.Normal(list(mu[l]), [ sigma ] * n_features , c)
elif self.covariance_type is 'diag' :
for l in range(n_components):
c = ot.CovarianceMatrix(n_features)
for i in range(n_features):
c[i,i] = cov[l, i]
collection[l] = ot.Normal(list(mu[l]), c)
elif self.covariance_type == 'tied':
# Same covariance for all clusters
c = ot.CovarianceMatrix(n_features)
for i in range(n_features):
for j in range(0, i+1):
c[i,j] = cov[i,j]
# Define the collection with the same covariance
for l in range(n_components):
collection[l] = ot.Normal(list(mu[l]), c)
else:
n_features = cov.shape[1]
for l in range(n_components):
c = ot.CovarianceMatrix(n_features)
for i in range(n_features):
for j in range(0, i+1):
c[i,j] = cov[l][i,j]
collection[l] = ot.Normal(list(mu[l]), c)
self._mixture = ot.Mixture(collection, weights)
return self
def get_mixture(self):
"""
Returns the Mixture object
"""
check_is_fitted(self)
return self._mixture
if __name__ == "__main__":
mu1 = 1.0
sigma1 = 0.5
mu2 = 3.0
sigma2 = 2.0
weights = [0.3, 0.7]
n1 = ot.Normal(mu1, sigma1)
n2 = ot.Normal(mu2, sigma2)
m = ot.Mixture([n1, n2], weights)
x = m.getSample(1000)
est_dist = MixtureFactory(random_state=1)
est_dist.fit(x)
print(est_dist.get_mixture())
I have actually tried this method and it works perfectly. On top of that the fit of the model through the SciKit GMM and the ulterior adjustment thanks to OpenTurns are very fast. I recommend future users to test several numbers of components and covariance matrix structures, as it will not take a lot of time and can substantially improve the goodness of fit to the data.
Thanks for the answer.
Here is a pure OpenTURNS solution. It is probably slower than the scikit-learn-based method, but it is more generic: you could use it to estimate the parameters of any mixture model, not necessarily a mixture of normal distributions.
The idea is to retrieve the log-likelihood function from the Mixture object and minimize it.
In the following, let us assume that s is the sample we want to fit the mixture on.
First, we need to build the mixture we want to estimate the parameters of. We can specify any valid set of parameters, it does not matter. In your example, you want a mixture of 2 normal distributions.
mixture = ot.Mixture([ot.Normal()]*2, [0.5]*2)
There is a small hurdle. All weights sum to 1, thus one of them is determined by the others: the solver must not be allowed to freely set it. The order of the parameters of an OpenTURNS Mixture is as follows:
weight of the first distribution;
parameters of the first distribution;
weight of the second distribution;
parameters of the second distribution:
...
You can view all parameters with mixture.getParameter() and their names with mixture.getParameterDescription(). The following is a helper function that:
takes as input the list containing of all mixture parameters except the weight of its first distribution;
outputs a Point containing all parameters including the weight of the first distribution.
def full(params):
"""
Point of all mixture parameters from a list that omits the first weight.
"""
params = ot.Point(params)
aux_mixture = ot.Mixture(mixture)
dist_number = aux_mixture.getDistributionCollection().getSize()
index = aux_mixture.getDistributionCollection()[0].getParameter().getSize()
list_weights = []
for num in range(1, dist_number):
list_weights.append(params[index])
index += 1 + aux_mixture.getDistributionCollection()[num].getParameter().getSize()
complementary_weight = ot.Point([abs(1.0 - sum(list_weights))])
complementary_weight.add(params)
return complementary_weight
The next function computes the opposite of the log-likelihood of a given list of parameters (except the first weight).
For the sake of numerical stability, it divides this value by the number of observations.
We will minimize this function in order to find the Maximum Likelihood Estimate.
def minus_log_pdf(params):
"""
- log-likelihood of a list of parameters excepting the first weight
divided by the number of observations
"""
aux_mixture = ot.Mixture(mixture)
full_params = full(params)
try:
aux_mixture.setParameter(full_params)
except TypeError:
# case where the proposed parameters are invalid:
# return a huge value
return [ot.SpecFunc.LogMaxScalar]
res = - aux_mixture.computeLogPDF(s).computeMean()
return res
To use OpenTURNS optimization facilities, we need to turn this function into a PythonFunction object.
OT_minus_log_pdf = ot.PythonFunction(mixture.getParameter().getSize()-1, 1, minus_log_pdf)
Cobyla is usually good at likelihood optimization.
problem = ot.OptimizationProblem(OT_minus_log_pdf)
algo = ot.Cobyla(problem)
In order to decrease chances of Cobyla being stuck on a local minimum, we are going to use MultiStart. We pick a starting set of parameters and randomly change the weights. The following helper function makes it easy:
def random_weights(params, nb):
"""
List of nb Points representing mixture parameters with randomly varying weights.
"""
aux_mixture = ot.Mixture(mixture)
full_params = full(params)
aux_mixture.setParameter(full_params)
list_params = []
for num in range(nb):
dirichlet = ot.Dirichlet([1.0] * aux_mixture.getDistributionCollection().getSize()).getRealization()
dirichlet.add(1.0 - sum(dirichlet))
aux_mixture.setWeights(dirichlet)
list_params.append(aux_mixture.getParameter()[1:])
return list_params
We pick 10 starting points and increase the number of maximum evaluations of the log-likelihood from 100 (by default) to 10000.
init = mixture.getParameter()[1:]
starting_points = random_weights(init, 10)
algo_multistart = ot.MultiStart(algo, starting_points)
algo_multistart.setMaximumEvaluationNumber(10000)
Let's run the solver and retrieve the result.
algo_multistart.run()
result = algo_multistart.getResult()
All that remains is to set the mixture's parameters to the optimal value.
We must not forget to add the first weight back!
optimal_parameters = result.getOptimalPoint()
mixture.setParameter(full(optimal_parameters))
Below is an alternative.
The first step creates a new GaussianMixture class, derived from PythonDistribution. The key point is to implement the computeLogPDF method and the set/getParameters methods. Notice that this parametrization of a mixture only has one single weight w.
class GaussianMixture(ot.PythonDistribution):
def __init__(self, mu1 = -5.0, sigma1 = 1.0, \
mu2 = 5.0, sigma2 = 1.0, \
w = 0.5):
super(GaussianMixture, self).__init__(1)
if w < 0.0 or w > 1.0:
raise ValueError('The weight is not in [0, 1]. w=%s.' % (w))
self.mu1 = mu2
self.sigma1 = sigma1
self.mu2 = mu2
self.sigma2 = sigma2
self.w = w
collDist = [ot.Normal(mu1, sigma1), ot.Normal(mu2, sigma2)]
weight = [w, 1.0 - w]
self.distribution = ot.Mixture(collDist, weight)
def computeCDF(self, x):
p = self.distribution.computeCDF(x)
return p
def computePDF(self, x):
p = self.distribution.computePDF(x)
return p
def computeQuantile(self, prob, tail = False):
quantile = self.distribution.computeQuantile(prob, tail)
return quantile
def getSample(self, size):
X = self.distribution.getSample(size)
return X
def getParameter(self):
parameter = ot.Point([self.mu1, self.sigma1, \
self.mu2, self.sigma2, \
self.w])
return parameter
def setParameter(self, parameter):
[mu1, sigma1, mu2, sigma2, w] = parameter
self.__init__(mu1, sigma1, mu2, sigma2, w)
return parameter
def computeLogPDF(self, sample):
logpdf = self.distribution.computeLogPDF(sample)
return logpdf
In order to create the distribution, we use the Distribution class:
gm = ot.Distribution(GaussianMixture())
Estimating the parameters of this distribution is straightforward with MaximumLikelihoodFactory. However, we must set the bounds, because sigma cannot be negative and that w is in (0, 1).
factory = ot.MaximumLikelihoodFactory(gm)
lowerBound = [0.0, 1.e-6, 0.0, 1.e-6, 0.01]
upperBound = [0.0, 0.0, 0.0, 0.0, 0.99]
finiteLowerBound = [False, True, False, True, True]
finiteUpperBound = [False, False, False, False, True]
bounds = ot.Interval(lowerBound, upperBound, finiteLowerBound, finiteUpperBound)
factory.setOptimizationBounds(bounds)
Then we configure the optimization solver.
solver = factory.getOptimizationAlgorithm()
startingPoint = [-4.0, 1.0, 7.0, 1.5, 0.3]
solver.setStartingPoint(startingPoint)
factory.setOptimizationAlgorithm(solver)
Estimating the parameters is based on the build method.
distribution = factory.build(sample)
There are two limitations with this implementation.
First, it is not as fast as it should be, because of a limitation of the PythonDistribution (see https://github.com/openturns/openturns/issues/1391).
Estimating the parameters may be difficult, because the problem may have local optimas that cannot be retrieved with the default algorithm in MaximumLikelihoodFactory. This kind of task is generally done with the EM algorithm.
I am trying to implement q-learning with an action-value approximation-function. I am using openai-gym and the "MountainCar-v0" enviroment to test my algorithm out. My problem is, it does not converge or find the goal at all.
Basically the approximator works like the following, you feed in the 2 features: position and velocity and one of the 3 actions in a one-hot encoding: 0 -> [1,0,0], 1 -> [0,1,0] and 2 -> [0,0,1]. The output is the action-value approximation Q_approx(s,a), for one specific action.
I know that usually, the input is the state (2 features) and the output layer contains 1 output for each action. The big difference that I see is that I have run the feed forward pass 3 times (one for each action) and take the max, while in the standard implementation you run it once and take the max over the output.
Maybe my implementation is just completely wrong and I am thinking wrong. Gonna paste the code here, it is a mess but I am just experimenting a bit:
import gym
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation
env = gym.make('MountainCar-v0')
# The mean reward over 20 episodes
mean_rewards = np.zeros(20)
# Feature numpy holder
features = np.zeros(5)
# Q_a value holder
qa_vals = np.zeros(3)
one_hot = {
0 : np.asarray([1,0,0]),
1 : np.asarray([0,1,0]),
2 : np.asarray([0,0,1])
}
model = Sequential()
model.add(Dense(20, activation="relu",input_dim=(5)))
model.add(Dense(10,activation="relu"))
model.add(Dense(1))
model.compile(optimizer='rmsprop',
loss='mse',
metrics=['accuracy'])
epsilon_greedy = 0.1
discount = 0.9
batch_size = 16
# Experience replay containing features and target
experience = np.ones((10*300,5+1))
# Ring buffer
def add_exp(features,target,index):
if index % experience.shape[0] == 0:
index = 0
global filled_once
filled_once = True
experience[index,0:5] = features
experience[index,5] = target
index += 1
return index
for e in range(0,100000):
obs = env.reset()
old_obs = None
new_obs = obs
rewards = 0
loss = 0
for i in range(0,300):
if old_obs is not None:
# Find q_a max for s_(t+1)
features[0:2] = new_obs
for i,pa in enumerate([0,1,2]):
features[2:5] = one_hot[pa]
qa_vals[i] = model.predict(features.reshape(-1,5))
rewards += reward
target = reward + discount*np.max(qa_vals)
features[0:2] = old_obs
features[2:5] = one_hot[a]
fill_index = add_exp(features,target,fill_index)
# Find new action
if np.random.random() < epsilon_greedy:
a = env.action_space.sample()
else:
a = np.argmax(qa_vals)
else:
a = env.action_space.sample()
obs, reward, done, info = env.step(a)
old_obs = new_obs
new_obs = obs
if done:
break
if filled_once:
samples_ids = np.random.choice(experience.shape[0],batch_size)
loss += model.train_on_batch(experience[samples_ids,0:5],experience[samples_ids,5].reshape(-1))[0]
mean_rewards[e%20] = rewards
print("e = {} and loss = {}".format(e,loss))
if e % 50 == 0:
print("e = {} and mean = {}".format(e,mean_rewards.mean()))
Thanks in advance!
There shouldn't be much difference between the actions as inputs to your network or as different outputs of your network. It does make a huge difference if your states are images for example. because Conv nets work very well with images and there would be no obvious way of integrating the actions to the input.
Have you tried the cartpole balancing environment? It is better to test if your model is working correctly.
Mountain climb is pretty hard. It has no reward until you reach the top, which often doesn't happen at all. The model will only start learning something useful once you get to the top once. If you are never getting to the top you should probably increase your time doing exploration. in other words take more random actions, a lot more...