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update of api docstrings across coach and tutorials [WIP] (#91)

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* updating the documentation website
* adding the built docs
* update of api docstrings across coach and tutorials 0-2
* added some missing api documentation
* New Sphinx based documentation
This commit is contained in:
Itai Caspi
2018-11-15 15:00:13 +02:00
committed by Gal Novik
parent 524f8436a2
commit 6d40ad1650
517 changed files with 71034 additions and 12834 deletions
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29
rl_coach/agents/dfp_agent.py
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@@ -81,6 +81,35 @@ class DFPMemoryParameters(EpisodicExperienceReplayParameters):
class DFPAlgorithmParameters(AlgorithmParameters):
"""
:param num_predicted_steps_ahead: (int)
Number of future steps to predict measurements for. The future steps won't be sequential, but rather jump
in multiples of 2. For example, if num_predicted_steps_ahead = 3, then the steps will be: t+1, t+2, t+4
:param goal_vector: (List[float])
The goal vector will weight each of the measurements to form an optimization goal. The vector should have
the same length as the number of measurements, and it will be vector multiplied by the measurements.
Positive values correspond to trying to maximize the particular measurement, and negative values
correspond to trying to minimize the particular measurement.
:param future_measurements_weights: (List[float])
The future_measurements_weights weight the contribution of each of the predicted timesteps to the optimization
goal. For example, if there are 6 steps predicted ahead, and a future_measurements_weights vector with 3 values,
then only the 3 last timesteps will be taken into account, according to the weights in the
future_measurements_weights vector.
:param use_accumulated_reward_as_measurement: (bool)
If set to True, the accumulated reward from the beginning of the episode will be added as a measurement to
the measurements vector in the state. This van be useful in environments where the given measurements don't
include enough information for the particular goal the agent should achieve.
:param handling_targets_after_episode_end: (HandlingTargetsAfterEpisodeEnd)
Dictates how to handle measurements that are outside the episode length.
:param scale_measurements_targets: (Dict[str, float])
Allows rescaling the values of each of the measurements available. This van be useful when the measurements
have a different scale and you want to normalize them to the same scale.
"""
def __init__(self):
super().__init__()
self.num_predicted_steps_ahead = 6