When I am learning tutorial 9, I am confusing the rl_actions.
Because on the program, the rl_actions is not initialized and definition.
Why there is a 'rl_actions' parameter of the _apply_rl_actions function and compute_reward function?
I also check the vehicle kernel code, about apply_acceleration function.
The original one is:
def apply_acceleration(self, veh_ids, acc):
"""See parent class."""
# to hand the case of a single vehicle
if type(veh_ids) == str:
veh_ids = [veh_ids]
acc = [acc]
for i, vid in enumerate(veh_ids):
if acc[i] is not None and vid in self.get_ids():
this_vel = self.get_speed(vid)
next_vel = max([this_vel + acc[i] * self.sim_step, 0])
self.kernel_api.vehicle.slowDown(vid, next_vel, 1e-3)
Look into flow/envs/base_env.py in the step method, this is where apply_rl_actions and compute_reward are called. All these 3 methods take as parameter the actions rl_actions to apply to the agents. These actions are provided by the RL algorithm. The shape of rl_actions is the one provided in the action_space method of your environment.
The RL algorithm automatically calls your step method at each step, giving it the actions to be applied. Flow's environments are actually encapsulated within a Gym environment, which is given to the RL algorithm. The RL algorithm can work with any Gym environment, which allows it to be very general, since all Gym environments have methods such as step, reset etc. If you want to know more about how this work, look into how to train a custom Gym environment.
Related
Goal: I want to train a PPO agent on a problem and determine its optimal value function for a range of observations. Later I plan to work with this value function (economic inequality research). The problem is sufficiently complex so that dynamic programming techniques no longer work.
Approach: In order to check, whether I get correct outputs for the value function, I have trained PPO on a simple problem, whose analytical solution is known. However, the results for the value function are rubbish, which is why I suspect that I have done sth wrong.
The code:
from keras import backend as k_util
...
parser = argparse.ArgumentParser()
# Define framework to use
parser.add_argument(
"--framework",
choices=["tf", "tf2", "tfe", "torch"],
default="tf",
help="The DL framework specifier.",
)
...
def get_rllib_config(seeds, debug=False, framework="tf") -> Dict:
...
def get_value_function(agent, min_state, max_state):
policy = agent.get_policy()
value_function = []
for i in np.arange(min_state, max_state, 1):
model_out, _ = policy.model({"obs": np.array([[i]], dtype=np.float32)})
value = k_util.eval(policy.model.value_function())[0]
value_function.append(value)
print(i, value)
return value_function
def train_schedule(config, reporter):
rllib_config = config["config"]
iterations = rllib_config.pop("training_iteration", 10)
agent = PPOTrainer(env=rllib_config["env"], config=rllib_config)
for _ in range(iterations):
result = agent.train()
reporter(**result)
values = get_value_function(agent, 0, 100)
print(values)
agent.stop()
...
resources = PPO.default_resource_request(exp_config)
tune_analysis = tune.Tuner(tune.with_resources(train_schedule, resources=resources), param_space=exp_config).fit()
ray.shutdown()
So first I get the policy (policy = agent.get_policy()) and run a forward pass with each of the 100 values (model_out, _ = policy.model({"obs": np.array([[i]], dtype=np.float32)})). Then, after each forward pass I use the value_function() method to get the output of the critic network and evaluate the tensor via keras backend.
The results:
True VF (analytical solution)
VF output of Rllib
Unfortunately you can see that the results are not that promising. Maybe I have missed a pre- or postprocessing step? Does the value_function() method even return the last layer of the critic network?
I am very grateful for any help!
It's not part of your script, but I assume that you have trained the policy before you attempt to get useful values out of it.
You are correct in assuming that the value_function() returns the output of the last layer of the critic network in RLlib's implementations.
Have a look at the value function metrics to see if it's actually learning anything (RLlib logs .../learner_stats/vf_loss and .../learner_stats/vf_explained_var)!
After training the model, I'd also try to query the model directly. If that looks better, something is likely off with the code you posted here.
I apologize in advance for the question in the title not being very clear. I'm trying to train a reinforcement learning policy using tf-agents in which there exists some unobservable stochastic variable that affects the state.
For example, consider the standard CartPole problem, but we add wind where the velocity changes over time. I don't want to train an agent that relies on having observed the wind velocity at each step; I instead want the wind to affect the position and angular velocity of the pole, and the agent to learn to adapt just as it would in the wind-free environment. In this example however, we would need the wind velocity at the current time to be correlated with the wind velocity at the previous time e.g. we wouldn't want the wind velocity to change from 10m/s at time t to -10m/s at time t+1.
The problem I'm trying to solve is how to track the state of the exogenous variable without making it part of the observation spec that gets fed into the neural network when training the agent. Any guidance would be appreciated.
Yes, that is no problem at all. Your environment object (a subclass of PyEnvironment or TFEnvironment) can do whatever you want within it. The observation_spec requirement is only related to the TimeStep that you output in the step and reset methods (more precisely in your implementation of the _step and _reset abstract methods).
Your environment however is completely free to have any additional attributes that you might want (like parameters to control wind generation) and any number of additional methods you like (like methods to generate the wind at this timestep according to self._wind_hyper_params). A quick schematic of your code would look like is below:
class MyCartPole(PyEnvironment):
def __init__(self, ..., wind_HP):
... # self._observation_spec and _action_spec can be left unchanged
self._wind_hyper_params = wind_HP
self._wind_velocity = 0
self._state = ...
def _update_wind_velocity(self):
self._wind_velocity = ...
def factor_in_wind(self):
self.state = ... #update according to wind
def _step(self, action):
... # self._state update computations
self._update_wind_velocity
self.factor_in_wind()
observations = self._state_to_observations()
...
I have been trying to implement this paper . Basically what I want to do is sum the per client loss and compare the same with previous epoch. Then for each constituent layer of the model compare the KL divergence between the weights of the server and the client model to get the layer specific parameter updates and then doing a softmax and to decide whether an adaptive update or a normal FedAvg approach is needed.
The algorithm is as follows-
FedMed
I tried to make use of the code here to build a custom federated avg process. I got the basic understanding that there are some tf.computations and some tff.computations which are involved. I get that I need to make changes in the orchestration logic in the run_one_round function and basically manipulate the client outputs to do adaptive averaging instead of the vanilla federated averaging. The client_update tf.computation function basically returns all the values that I need i.e the weights_delta (can be used for client based model weights), model_output(which can be used to calculate the loss).
But I am not sure where exactly I should make the changes.
#tff.federated_computation(federated_server_state_type,
federated_dataset_type)
def run_one_round(server_state, federated_dataset):
server_message = tff.federated_map(server_message_fn, server_state)
server_message_at_client = tff.federated_broadcast(server_message)
client_outputs = tff.federated_map(
client_update_fn, (federated_dataset, server_message_at_client))
weight_denom = client_outputs.client_weight
# todo
# instead of using tff.federated_mean I wish to do a adaptive aggregation based on the client_outputs.weights_delta and server_state model
round_model_delta = tff.federated_mean(
client_outputs.weights_delta, weight=weight_denom)
#client_outputs.weights_delta has all the client model weights.
#client_outputs.client_weight has the number of examples per client.
#client_outputs.model_output has the output of the model per client example.
I want to make use of the server model weights using server_state object.
I want to calculate the KL divergence between the weights of server model and each client's model per layer. Then use a relative weight to aggregate the client weights instead of vanilla federated averaging.
Instead of using tff.federated_mean I wish to use a different strategy basically an adaptive one based on the algorithm above.
So I needed some suggestions on how to go about implementing this.
Basically what I want to do is :
1)Sum all the values of client losses.
2)Calculate the KL divergence per layerbasis of all the clients with server and then determine whether to use adaptive optimization or FedAvg.
Also is there a way to manipulate this value as a python value which will be helpful for debugging purposes( I tried to use tf.print but that was not helpful either). Thanks!
Simplest option: compute weights for mean on clients
If I read the algorithm above correctly, we need only compute some weights for a mean on-the-fly. tff.federated_mean accepts an optional CLIENTS-placed weight argument, so probably the simplest option here is to compute the desired weights on the clients and pass them in to the mean.
This would look something like (assuming the appropriate definitions of the variables used below, which we will comment on):
#tff.federated_computation(...)
def round_function(...):
...
# We assume there is a tff.Computation training_fn that performs training,
# and we're calling it here on the correct arguments
trained_clients = tff.federated_map(training_fn, clients_placed_arguments)
# Next we assume there is a variable in-scope server_model,
# representing the 'current global model'.
global_model_at_clients = tff.federated_broadcast(server_model)
# Here we assume a function compute_kl_divergence, which takes
# two structures of tensors and computes the KL divergence
# (as a scalar) between them. The two arguments here are clients-placed,
# so the result will be as well.
kl_div_at_clients = tff.federated_map(compute_kl_divergence,
(global_model_at_clients, trained_clients))
# Perhaps we wish to not use raw KL divergence as the weight, but rather
# some function thereof; if so, we map a postprocessing function to
# the computed divergences. The result will still be clients-placed.
mean_weight = tff.federated_map(postprocess_divergence, kl_div_at_clients)
# Now we simply use the computed weights in the mean.
return tff.federated_mean(trained_clients, weight=mean_weight)
More flexible tool: tff.federated_reduce
TFF generally encourages algorithm developers to implement whatever they can 'in the aggregation', and as such exposes some highly customizable primitives like tff.federated_reduce, which allow you to run arbitrary TensorFlow "in the stream" between clients and server. If the above reading of the desired algorithm is incorrect and something more involved is needed, or you wish to flexibly experiment with totally different notions of aggregation (something TFF encourages and is designed to support), this may be the option for you.
In TFF's heuristic typing language, tff.federated_reduce has signature:
<{T}#CLIENTS, U, (<U, T> -> U)> -> U#SERVER
Meaning, federated_reduce take a value of type T placed at the clients, a 'zero' in a reduction algebra of type U, and a function accepting a U and a T and producing a U, and applies this function 'in the stream' on the way between clients and server, producing a U placed at the server. The function (<U, T> -> U) will be applied to the partially accumulated value U, and the 'next' element in the stream T (note however that TFF does not guarantee ordering of these values), returning another partially accumulated value U. The 'zero' should represent whatever 'partially accumulated' means over the empty set in your application; this will be the starting point of the reduction.
Application to this problem
The components
Your reduction function needs access to two pieces of data: the global model state and the result of training on a given client. This maps quite nicely to the type T. In this application, we will have something like:
T = <server_model=server_model_type, trained_model=trained_model_type>
These two types are likely to be the same, but may not necessarily be so.
Your reduction function will accept the partial aggregate, your server model and your client-trained model, returning a new partial aggregate. Here we will start assuming the same reading of the algorithm as above, that of a weighted mean with particular weights. Generally, the easiest way to compute a mean is to keep two accumulators, one for numerator and one for denominator. This will affect the choice of zero and reduction function below.
Your zero should contain a structure of tensors with value 0 mapping to the weights of your model--this will be the numerator. This would be generated for you if you had an aggregation like tff.federated_sum (as TFF knows what the zero should be), but for this case you'll have to get your hands on such a tensor yourself. This shouldn't be too hard with tf.nest.map_structure and tf.zeros_like.
For the denominator, we will assume we just need a scalar. TFF and TF are much more flexible than this--you could keep a per-layer or per-parameter denominator if desired--but for simplicity we will assume that we just want to divide by a single float in the end.
Therefore our type U will be something like:
U = <numerator=server_model_type, denominator=tf.float32>
Finally we come to our reduction function. It will be more or less a different composition of the same pieces above; we will make slightly tighter assumptions about them here (in particular, that all the local functions are tff.tf_computations--a technical assumption, arguably a bug on TFF). Our reduction function will be along the lines (assuming appropriate type aliases):
#tff.tf_computation(U, T)
def reduction(partial_accumulate, next_element):
kl_div = compute_kl_divergence(
next_element.server_model, next_element.trained_model)
weight = postprocess_divergence(kl_div)
new_numerator = partial_accumulate.numerator + weight * next_element.trained_model
new_denominator = partial_accumulate.denominator + weight
return collections.OrderedDict(
numerator=new_numerator, denominator=new_denominator)
Putting them together
The basic outline of a round will be similar to the above; but we have put more computation 'in the stream', and consequently there wil be less on the clients. We assume here the same variable definitions.
#tff.federated_computation(...)
def round_function(...):
...
trained_clients = tff.federated_map(training_fn, clients_placed_arguments)
global_model_at_clients = tff.federated_broadcast(server_model)
# This zip I believe is not necessary, but it helps my mental model.
reduction_arg = tff.federated_zip(
collections.OrderedDict(server_model=global_model_at_clients,
trained_model=trained_clients))
# We assume a zero as specified above
return tff.federated_reduce(reduction_arg,
zero,
reduction)
I think many other people like me might be interested in how they can use GPFlow for their special problems. The key is how GPFlow is customizable, and a good example would be very helpful.
In my case, I read and tried lots of comments in raised issues without any real success. Setting kernel model parameters is not straightforward (creating with default values, and then do it via the delete object method). Transform method is vague.
It would be really helpful if you could add an example showing. how one can initialize and set bounds of an anisotropic kernel model (length-scales values and bounds, variances, ...) and specially adding observations error (as an array-like alpha parameter)
If you just want to set a value, then you can do
model = gpflow.models.GPR(np.zeros((1, 1)),
np.zeros((1, 1)),
gpflow.kernels.RBF(1, lengthscales=0.2))
Alternatively
model = gpflow.models.GPR(np.zeros((1, 1)),
np.zeros((1, 1)),
gpflow.kernels.RBF(1))
model.kern.lengthscales = 0.2
If you want to change the transform, you either need to subclass the kernel, or you can also do
with gpflow.defer_build():
model = gpflow.models.GPR(np.zeros((1, 1)),
np.zeros((1, 1)),
gpflow.kernels.RBF(1))
transform = gpflow.transforms.Logistic(0.1, 1.))
model.kern.lengthscales = gpflow.params.Parameter(0.3, transform=transform)
model.compile()
You need the defer_build to stop the graph being compiled before you've changed the transform. Using the approach above, the compilation of the tensorflow graph is delayed (until the explicit model.compile()) so is built with the intended bounding transform.
Using an array parameter for likelihood variance is outside the scope of gpflow. For what it's worth (and because it has been asked about before), that particular model is especially problematic as it is not clear how test points are defined.
Setting kernel parameters can be done using the .assign() function, or through direct assignment. See the notebook https://github.com/GPflow/GPflow/blob/develop/doc/source/notebooks/understanding/tf_graphs_and_sessions.ipynb. You do not need to delete a parameter to assign a new value to it.
If you want to have per-datapoint noise, you will need to implement your own custom likelihood, which you can do by taking Gaussian likelihood in likelihoods.py as an example.
If by "bounds" you mean limiting the optimisation range for a parameter, you can use the Logistic transform. If you want to pass in a custom transformation for a parameter, you can pass a constructed Parameter object into constructors with a custom transform. Alternatively you can assign a newly created Parameter with a new transform to the model.
Here is more information on how to access and change GPflow parameters: viewing, getting and settings parameters documentation.
Extra bit for #user1018464 answer about replacing transform in existing parameter: changing transformation is a bit tricky, you can't change transformation once a model was compiled in TensorFlow.
E.g.
likelihood = gpflow.likelihoods.Gaussian()
likelihood.variance.transform = gpflow.transforms.Logistic(1., 10.)
----
GPflowError: Parameter "Gaussian/variance" has already been compiled.
Instead you have to reset GPflow object:
likelihood = gpflow.likelihoods.Gaussian() # All tensors compiled
likelihood.clear()
likelihood.variance.transform = gpflow.transforms.Logistic(2, 5)
likelihood.variance = 2.5
likelihood.compile()
I am wondering whether there is an easy way to define a user defined tensorflow operation if it consists only of chained tensorflow operations. This is just to make code from being unnecessarily long, especially if the same operations must be performed on similar objects:
For example, if I want to define a feed forward mechanism on a neural network with 2 hidden layers, I will need to do this:
layer1_output = tf.nn.relu(tf.nn.matmul(input,weights1) + biases1)
layer2_output = tf.nn.relu(tf.nn.matmul(layer1_output,weights2) + biases2)
layer3_output = tf.nn.softmax(tf.nn.matmul(layer2_output,weights3) + biases2)
However, this oftentimes needs to be done for validation and test sets also, so I would like to define a function which would let me do all the operations in a single swoop so I could get something like this:
train_output = feed_forward(input_train)
test_output = feed_forward(input_test)...
This seems like something simple to do, but I can't seem to find the documentation.
The standard way to do this is just to define a Python function the builds your network:
def feed_forward(input_data):
layer1_output = tf.nn.relu(tf.nn.matmul(input_data, weights1) + biases1)
layer2_output = tf.nn.relu(tf.nn.matmul(layer1_output, weights2) + biases2)
layer3_output = tf.nn.softmax(tf.nn.matmul(layer2_output, weights3) + biases2)
return layer3_output
Depending on how you define your weights and biases variables, you might pass them in as arguments to the function, or use tf.get_variable() to handle the construction and sharing of variables between different calls to the function.