deploy: 1036bd2

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 56ec7544e57e96ef6ff56b72104934f0
+tags: 645f666f9bcd5a90fca523b33c5a78b7

CNAME

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+rktl.purduearc.com

_sources/autoapi/index.rst.txt

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+API Reference
+=============
+
+This page contains auto-generated API reference documentation [#f1]_.
+
+.. toctree::
+   :titlesonly:
+
+   /autoapi/rktl_autonomy/index
+   /autoapi/rktl_planner/index
+   /autoapi/simulator/index
+   /autoapi/visualizer/index
+
+.. [#f1] Created with `sphinx-autoapi <https://github.com/readthedocs/sphinx-autoapi>`_

_sources/autoapi/rktl_autonomy/_ros_interface/index.rst.txt

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+:py:mod:`rktl_autonomy._ros_interface`
+======================================
+
+.. py:module:: rktl_autonomy._ros_interface
+
+.. autoapi-nested-parse::
+
+   This handles the base behavior of manipulating time and interfacing with ROS.
+   It gets subclassed to customize it to a specific ROS network's environment.
+
+   License:
+     BSD 3-Clause License
+     Copyright (c) 2023, Autonomous Robotics Club of Purdue (Purdue ARC)
+     All rights reserved.
+
+
+
+Module Contents
+---------------
+
+Classes
+~~~~~~~
+
+.. autoapisummary::
+
+   rktl_autonomy._ros_interface.ROSInterface
+
+
+
+
+.. py:exception:: SimTimeException
+
+   Bases: :py:obj:`Exception`
+
+   For when advancing sim time does not go as planned.
+
+
+.. py:class:: ROSInterface(node_name='gym_interface', eval=False, launch_file=None, launch_args=[], run_id=None)
+
+   Bases: :py:obj:`gym.Env`
+
+   Extension of the Gym environment class for all specific interfaces
+   to extend. This class handles logic regarding timesteps in ROS, and
+   allows users to treat any ROS system as a Gym environment once the
+   interface is created.
+
+   All classes extending this for a particular environment must do the following:
+       - implement all abstract properties:
+           - action_space
+           - observation_space
+       - implement all abstract methods:
+           - _reset_env()
+           - _reset_self()
+           - _has_state()
+           - _clear_state()
+           - _get_state()
+           - _publish_action()
+       - notify _cond when _has_state() may have turned true
+
+   .. py:property:: action_space
+      :abstractmethod:
+
+      The Space object corresponding to valid actions.
+
+
+   .. py:property:: observation_space
+      :abstractmethod:
+
+      The Space object corresponding to valid observations.
+
+
+   .. py:method:: step(action)
+
+      Implementation of gym.Env.step. This function will intentionally block
+      if the ROS environment is not ready.
+
+      Run one timestep of the environment's dynamics. When end of
+      episode is reached, you are responsible for calling `reset()`
+      to reset this environment's state.
+      Accepts an action and returns a tuple (observation, reward, done, info).
+      Args:
+          action (object): an action provided by the agent
+      Returns:
+          observation (object): agent's observation of the current environment
+          reward (float) : amount of reward returned after previous action
+          done (bool): whether the episode has ended, in which case further step() calls will return undefined results
+          info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)
+
+
+   .. py:method:: reset()
+
+      Resets the environment to an initial state and returns an initial observation.
+
+      Note that this function should not reset the environment's random
+      number generator(s); random variables in the environment's state should
+      be sampled independently between multiple calls to `reset()`. In other
+      words, each call of `reset()` should yield an environment suitable for
+      a new episode, independent of previous episodes.
+      Returns:
+          observation (object): the initial observation.
+
+
+   .. py:method:: __step_time_and_wait_for_state(max_retries=1)
+
+      Step time until a state is known.
+
+
+   .. py:method:: __wait_once_for_state()
+
+      Wait and allow other threads to run.
+
+
+   .. py:method:: _reset_env()
+      :abstractmethod:
+
+      Reset environment for a new episode.
+
+
+   .. py:method:: _reset_self()
+      :abstractmethod:
+
+      Reset internally for a new episode.
+
+
+   .. py:method:: _has_state()
+      :abstractmethod:
+
+      Determine if the new state is ready.
+
+
+   .. py:method:: _clear_state()
+      :abstractmethod:
+
+      Clear state variables / flags in preparation for new ones.
+
+
+   .. py:method:: _get_state()
+      :abstractmethod:
+
+      Get state tuple (observation, reward, done, info).
+
+
+   .. py:method:: _publish_action(action)
+      :abstractmethod:
+
+      Publish an action to the ROS network.
+
+
+

_sources/autoapi/rktl_autonomy/cartpole_direct_interface/index.rst.txt

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+:py:mod:`rktl_autonomy.cartpole_direct_interface`
+=================================================
+
+.. py:module:: rktl_autonomy.cartpole_direct_interface
+
+.. autoapi-nested-parse::
+
+   Cartpole is a very simple environment included within OpenAI Gym. There is not
+   much of a reason to use it, except for verifying the functionality of this
+   library. Two different interfaces are provided, `CartpoleInterface` and
+   `CartpoleDirectInterface`. The former uses the `ROSInterface` class and the
+   latter directly owns the Gym environment. To verify the `ROSInterface` worked,
+   it was confirmed that they both behave the same way.
+
+   License:
+     BSD 3-Clause License
+     Copyright (c) 2023, Autonomous Robotics Club of Purdue (Purdue ARC)
+     All rights reserved.
+
+
+
+Module Contents
+---------------
+
+Classes
+~~~~~~~
+
+.. autoapisummary::
+
+   rktl_autonomy.cartpole_direct_interface.CartpoleDirectInterface
+
+
+
+
+.. py:class:: CartpoleDirectInterface(eval=False, launch_file=['rktl_autonomy', 'cartpole_train.launch'], launch_args=[], run_id=None)
+
+   Bases: :py:obj:`rktl_autonomy.ROSInterface`
+
+   ROS interface for the cartpole game.
+
+   .. py:property:: action_space
+
+      The Space object corresponding to valid actions.
+
+
+   .. py:property:: observation_space
+
+      The Space object corresponding to valid observations.
+
+
+   .. py:method:: _reset_env()
+
+      Reset environment for a new training episode.
+
+
+   .. py:method:: _reset_self()
+
+      Reset internally for a new episode.
+
+
+   .. py:method:: _has_state()
+
+      Determine if the new state is ready.
+
+
+   .. py:method:: _clear_state()
+
+      Clear state variables / flags in preparation for new ones.
+
+
+   .. py:method:: _get_state()
+
+      Get state tuple (observation, reward, done, info).
+
+
+   .. py:method:: _publish_action(action)
+
+      Publish an action to the ROS network.
+
+
+

_sources/autoapi/rktl_autonomy/cartpole_interface/index.rst.txt

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+:py:mod:`rktl_autonomy.cartpole_interface`
+==========================================
+
+.. py:module:: rktl_autonomy.cartpole_interface
+
+.. autoapi-nested-parse::
+
+   Cartpole is a very simple environment included within OpenAI Gym. There is not
+   much of a reason to use it, except for verifying the functionality of this
+   library. Two different interfaces are provided, `CartpoleInterface` and
+   `CartpoleDirectInterface`. The former uses the `ROSInterface` class and the
+   latter directly owns the Gym environment. To verify the `ROSInterface` worked,
+   it was confirmed that they both behave the same way.
+
+   License:
+     BSD 3-Clause License
+     Copyright (c) 2023, Autonomous Robotics Club of Purdue (Purdue ARC)
+     All rights reserved.
+
+
+
+Module Contents
+---------------
+
+Classes
+~~~~~~~
+
+.. autoapisummary::
+
+   rktl_autonomy.cartpole_interface.CartpoleActions
+   rktl_autonomy.cartpole_interface.CartpoleInterface
+
+
+
+
+.. py:class:: CartpoleActions
+
+   Bases: :py:obj:`enum.IntEnum`
+
+   Possible actions for deep learner.
+
+   .. py:attribute:: LEFT
+      :value: 0
+
+
+
+   .. py:attribute:: RIGHT
+
+      
+
+   .. py:attribute:: SIZE
+
+      
+
+
+.. py:class:: CartpoleInterface(eval=False, launch_file=['rktl_autonomy', 'cartpole_train.launch'], launch_args=[], run_id=None)
+
+   Bases: :py:obj:`rktl_autonomy.ROSInterface`
+
+   ROS interface for the cartpole game.
+
+   .. py:property:: action_space
+
+      The Space object corresponding to valid actions.
+
+
+   .. py:property:: observation_space
+
+      The Space object corresponding to valid observations.
+
+
+   .. py:method:: _reset_env()
+
+      Reset environment for a new training episode.
+
+
+   .. py:method:: _reset_self()
+
+      Reset internally for a new episode.
+
+
+   .. py:method:: _has_state()
+
+      Determine if the new state is ready.
+
+
+   .. py:method:: _clear_state()
+
+      Clear state variables / flags in preparation for new ones.
+
+
+   .. py:method:: _get_state()
+
+      Get state tuple (observation, reward, done, info).
+
+
+   .. py:method:: _publish_action(action)
+
+      Publish an action to the ROS network.
+
+
+   .. py:method:: _obs_cb(obs_msg)
+
+      Callback for observation of game.
+
+
+   .. py:method:: _reward_cb(reward_msg)
+
+      Callback for reward of game.
+
+
+   .. py:method:: _done_cb(done_msg)
+
+      Callback for if episode is done.
+
+
+