FAQ.md

TensorForce FAQ

1. How can I use TensorForce in a new environment or application?

This depends on the control flow of your problem. For most simulations, it is convenient to implement a binding to the TensorForce Environment class (various examples in contrib). The advantage of this is that it allows you to use the existing execution scripts, in particular the Runner utility. The general workflow is to copy one of the example scripts in the examples folder which parse arguments and call the runner. The runner will then control execution by calling your environment for the specified number of steps.

If you have a real-world environment, things are generally different as you may not be able to delegate control flow to TensorForce. Instead, your external application might use TensorFlow as a library, and call act() and observe() when new data is available. Consider the quickstart example in the readme.

2. Why is my algorithm not learning?

Generally, this is either because there is a bug in our implementation or a problem in your configuration or application. The reality of reinforcement learning is that getting things to work is very difficult and we will not be able to tell you why your specific thing is not working. Newcomers with some experience in deep learning, where successfully training small networks is easy, often carry over this expectation to reinforcement learning. Issues that simply ask for configuration help without doing some research (small ablation analysis, using some known hyper-parameters from papers, or reasonable argument why something should work) will be closed with reference to this document.

Please appreciate that for almost every problem, none of the default configurations will work, usually because batch sizes and learning rates are wrong, or you need vastly more data. Substantial practical experience is required to get an intuition for what is possible with which amount of data for a given problem. Reproducing papers is extremely difficult. From a user perspective, the expectation should be that to get an algorithm to work on any problem, hyper-parameter tuning is required.

That being said, there are small implementation issues in some of the Q-models due to the move to full TensorFlow code, and the current recommendation is to use PPO unless there is a good reason not to.

3. Can you implement paper X?

We get many feature requests and the answer to most is "maybe". Reinforcement learning is a very active, fast moving field, but at the same time very immature as a technology. This means most new approaches will likely not be practically relevant going forward, this is the nature of research, even though these approaches inform the development of more mature algorithms later.

Further, new approaches are often unnecessarily complex, which often only becomes clear in hindsight. For example, PPO both performs much better than TRPO and is much simpler to implement. TensorForce is not meant to be a collection of every new available trick. This is in particular not possible due to the architecture choices we have made to design full TensorFlow reinforcement learning graphs. Integrating new techniques into this architecture tends to require much higher effort than just implementing the new method from scratch in a separate script.

For this reason, we mostly do not implement new papers straight away unless they are extremely convincing and have a good implementation difficulty to return ratio.

4. How can I use an evolutionary or natural gradient optimizer?

By changing the type of the optimizer where appropriate. For example, a vanilla policy gradient may use an evolutionary optimizer via:

optimizer=dict(
    type='evolutionary',
    learning_rate=1e-2
)

and a natural gradient optimizer via:

optimizer=dict(
    type='natual_gradient',
    learning_rate=1e-2
)

Please note that not every model can sensibly make use of every optimizer. Read up on the individual model, for example, TRPO is by default a natural gradient model.

5. What is deterministic mode? How does it relate to evaluation?

The deterministic flag on act only concerns the action selection. It does not affect whether training is performed or not. Training is controlled via observe calls. The deterministic flag is relevant with regard to stochastic and deterministic policies. For example, policy gradient models typically assume a stochastic policy during training (unless when using deterministic policy gradients). Q-models, which we have also implemented inheriting from a distribution model (see our blog posts on architecture), deterministically sample their action via their greedy maximisation.

When evaluating the final trained model, one would typically act deterministically to avoid sampling random actions.

6. How do I specify multiple actions?

Actions can either be specified as single actions which just require a dict specifying a type and shape, or a num_actions parameter in the case of discrete actions. A shape can be used to specify multiple actions of the same type, e.g. actions=dict(type='float', shape=(5,), min=-2, max=2) specifies 5 float actions with the same range. Please note that using the min/max parameter results in a Beta distribution instead of a Gaussian distribution being used for continuous actions. If a single action is desired, use shape=().

The parameter num_actions is only used for discrete actions and describes the number of different options for a single discrete action. That is, dict(type='int', shape=(2,), num_actions=4) means the agent will output two actions per step, each giving values in [0, 1, 2, 3].

There are no restrictions on combining multiple action types. Available action types are float,int,bool. In the case of multiple actions, the agent expects a dict containing action names and their types. For example,

actions=dict(
   discrete_action=dict(type='int', num_actions=15),
   continous_action=dict(type='float', shape=()),
)

will lead to the agent outputting the following action dict (example action):

actions = agent.act(states=state)

>>> print actions
>>> dict(discrete_action=3, continuous_action=0.332)

In the case of multiple actions, the values will be arrays. It is a good idea to use expressive action names within your application to keep track of each action and its purpose.

7. How do I preload values in my model?

There are scenarios where you might want to initialize the model or parts of the model with values learnt from training on another task or completely outside of the Tensorforce framework. Here is a simple recipe of how to do this:

1. You need to define the component of the model that you want to save or restore as SavableComponent. The example below adds the SavableComponent mixin to LayeredNetwork so that the network component can be saved and restored independently.

   from tensorforce.util import SavableComponent
   from tensorforce.core.networks import LayeredNetwork
   
   
   class SavableNetwork(LayeredNetwork, SavableComponent):
       """
       Minimal implementation of a Network that can be saved and restored independently of the Model.
       """
       def get_savable_variables(self):
           return super(SavableNetwork, self).get_variables(include_nontrainable=False)
   
       def _get_base_variable_scope(self):
           return self.apply.variable_scope_name

2. Specify the savable components in your agent's definition:

   from tensorforce.agents import PPOAgent
   
   agent = PPOAgent(
       update_mode=dict(...),
       memory=dict(...),
       step_optimizer=dict(...),
       states=...,
       actions=...,
       network=dict(
           # The type we defined above
           type=SavableNetwork,
           layers=[...]
       )
   )

3. You can utilize the model's save_component and restore_component methods to save or restore the parameters of the individual components. Following the example you can restore the network values right after the agent is initialized like this:

   agent.model.restore_component(
       component_name="network",
       save_path=...
   )

   Note the use of "network" as the component's name. You can also use the constants defined by the specific model implementation for instance: agent.model.COMPONENT_NETWORK or DistributionModel.COMPONENT_NETWORK.

Note on variable names
If you plan to use parameters trained and saved outside Tensorforce you need to make sure that the variables are saved with names that match the names used by Tensorforce. For instance:

layer = tf.layers.Dense(units=output_size)

# Train the network
...

# Save the parameters
var_map = {
    "dense0/apply/linear/apply/W:0": layer.kernel,
    "dense0/apply/linear/apply/b:0": layer.bias
}
saver = tf.train.Saver(var_list=var_map)
saver.save(sess=sess, write_meta_graph=False, save_path=save_path)