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About
rcognita is a flexibly configurable framework for agent-enviroment simulation with a menu of predictive and safe reinforcement learning controllers.
A detailed documentation is available here <https://aidynamicaction.github.io/rcognita/>__.
Example run with a mobile robot simulation
Table of content
Installation <#Installation>__Basic <#Basic>__With model estimation tools <#With-model-estimation-tools>__
General description <#General-description>__Usage <#Usage>__Settings <#Settings>__Advanced customization <#Advanced-customization>__Experimental things <#Experimental-things>__
Related literature <#Related-literature>__Closing remarks <#Closing-remarks>__
Installation
Basic
To table of content <#Table-of-content>__
Run in terminal:
::
pip3 install rcognitaAlternatively, one can install the package direcly form the master branch. The following instruction is for Unix-based systems, assuming a terminal and Python3 interpreter.
::
git clone https://github.com/AIDynamicAction/rcognita
cd rcognita
python3 setup.py installNotice that your Python 3 interpreter might be called something else,
say, just python.
With model estimation tools
To table of content <#Table-of-content>__
The package was tested with online model estimation using
SIPPY <https://github.com/CPCLAB-UNIPI/SIPPY>__. The respective
functionality is implemented and enabled via is_est_model. Related
parameters can be found in the documentation of the CtrlOptPred
class.
Installing dependencies
To install ``SIPPY``, first take care of the dependencies:
Ubuntu/Debian:
^^^^^^^^^^^^^^
::
sudo apt-get install -y build-essential gfortran cmake libopenblas-dev
Arch
^^^^
::
pacman -Sy gcc gcc-fortran cmake base-devel openblas
Installing ``scikit-build``::
pip install scikit-buildor, using Anaconda,
::
conda install scikit-buildInstalling rcognita with SIPPY
::
pip3 install rcognita[SIPPY]
General description
===================
`To table of content <#Table-of-content>`__
``rcognita`` Python package is designed for hybrid simulation of agents
and environments (generally speaking, not necessarily reinforcement
learning agents). Its main idea is to have an explicit implementation of
sampled controls with user-defined sampling time specification. The
package consists of several modules, namely, ``controllers``,
``loggers``, ``models``, ``simulator``, ``systems``, ``utilities``,
``visuals`` and a collection of main modules (presets) for each
agent-environment configuration.
`This flowchart <./flowcharts/rcognita-flowchart-CtrlOptPred.pdf>`__
shows interaction of the core ``rcognita`` classes contained in the said
modules (the latter are not shown on the diagram).
The main module is a preset, on the flowchart a 3-wheel robot. It
initializes the system (the environment), the controllers (the agents,
e. g., a safe agent, a benchmarking agent, a reinforcement learning
agent etc.), the visualization engine called animator, the logger and
the simulator. The latter is a multi-purpose device for simulating
agent-environment loops of different types (specified by ``sys_type``).
Depending on ``sys_type``, the environment can either be described by a
differential equation (including stochastic ones), a difference equation
(for discrete-time systems), or by a probability distribution (for,
e.g., Markov decision processes).
The parameter ``dt`` determines the maximal step size for the numerical
solver in case of differential equations. The main method of this class
is ``sim_step`` which performs one solver step, whereas reset
re-initializes the simulator after an episode.
The ``Logger`` class is an interface defining stubs of a
print-to-console method print sim step, and print-to-file method log
data row, respectively. Concrete loggers realize these methods.
A similar class inheritance scheme is used in ``Animator``, and
``System``. The core data of ``Animator``\ ’s subclasses are
``objects``, which include entities to be updated on the screen, and
their parameters stored in ``pars``.
A concrete realization of a system interface must realize ``sys_dyn``,
which is the “right-handside” of the environment description, optionally
disturbance dynamics via ``disturb_dyn``, optionally controller dynamics
(if the latter is, e.g., time-varying), and the output function ``out``.
The method ``receive_action`` gets a control action and stores it.
Everything is packed together in the ``closed_loop_rhs`` for the use in
``Simulator``.
Finally, the ``controllers`` module contains various agent types. One of
them is ``CtrlOptPred`` – the class of predictive objective-optimizing
agents (model-predictive control and predictive reinforcement learning)
as shown in `this
flowchart <./flowcharts/rcognita-flowchart-CtrlOptPred.pdf>`__. Notice
it contains an explicit specification of the sampling time ``dt``.
The method ``_critic`` computes a model of something related to the
value, e.g., value function, Q-function or advantage. In turn,
``_critic_cost`` defines a cost (loss) function to fir the critic
(commonly based on temporal errors). The method ``_critic_optimizer``
actually optimizes the critic cost. The principle is analogous with the
actor, except that it optimizes an objective along a prediction horizon.
The details can be found in the code documentation. The method
``compute_action`` essentially watches the internal clock and performs
an action updates when a time sample has elapsed.
Auxiliary modules of the package are ``models`` and ``utilities`` which
provide auxiliary functions and data structures, such as neural
networks.
Usage
=====
`To table of content <#Table-of-content>`__
After the package is installed, you may just ``python`` run one of the
presets found `here <./presets>`__, say,
::
python3 main_3wrobot_NI.py
This will call the preset with default settings, description of which
can be found in the preset accordingly.
The naming convention is ``main_ACRONYM``, where ``ACRONYM`` is actually
related to the system (environment). You may create your own by analogy.
For configuration of hyper-parameters, just call help on the required
preset, say,
::
python3 main_3wrobot_NI.py -h
Settings
--------
`To table of content <#Table-of-content>`__
Some key settings are described below (full description is available via
``-h`` option).
+-------------------------+-----------+--------------------------------------------------------+
| Parameter | Type | Description |
+=========================+===========+========================================================+
| ``ctrl_mode`` | string | Controller mode |
+-------------------------+-----------+--------------------------------------------------------+
| ``dt`` | number | Controller sampling time |
+-------------------------+-----------+--------------------------------------------------------+
| ``t1`` | number | Final time |
+-------------------------+-----------+--------------------------------------------------------+
| ``state_init`` | list | Initial state |
+-------------------------+-----------+--------------------------------------------------------+
| ``is_log_data`` | binary | Flag to log data |
+-------------------------+-----------+--------------------------------------------------------+
| ``is_visualization`` | binary | Flag to produce graphical output |
+-------------------------+-----------+--------------------------------------------------------+
| ``is_print_sim_step`` | binary | Flag to print simulation step data |
+-------------------------+-----------+--------------------------------------------------------+
| ``is_est_model`` | binary | If a model of the system is to be estimated online |
+-------------------------+-----------+--------------------------------------------------------+
| ``Nactor`` | integer | Horizon length (in steps) for predictive controllers |
+-------------------------+-----------+--------------------------------------------------------+
| ``stage_obj_struct`` | string | Structure of running objective function |
+-------------------------+-----------+--------------------------------------------------------+
| ``Ncritic`` | integer | Critic stack size (number of TDs) |
+-------------------------+-----------+--------------------------------------------------------+
| ``gamma`` | number | Discount factor |
+-------------------------+-----------+--------------------------------------------------------+
| ``critic_struct`` | string | Structure of critic features |
+-------------------------+-----------+--------------------------------------------------------+
| ``actor_struct`` | string | Structure of actor features |
+-------------------------+-----------+--------------------------------------------------------+
Advanced customization
----------------------
`To table of content <#Table-of-content>`__
- **Custom environments**: realize ``system`` interface in the
``systems`` module. You might need nominal controllers for that, as
well as an animator, a logger etc.
- **Custom running cost**: adjust ``rcost`` in controllers
- **Custom AC method**: simplest way -- by adding a new mode and
updating ``_actor_cost``, ``_critic_cost`` and, possibly, ``_actor``,
``_critic``. For deep net AC structures, use, say,
`PyTorch <https://pytorch.org/>`__
- **Custom model estimator**: so far, the framework offers a
state-space model structure. You may use any other one. In case of
neural nets, use, e.g., `PyTorch <https://pytorch.org/>`__
Experimental things
-------------------
`To table of content <#Table-of-content>`__
An interface for dynamical controllers, which can be considered as
extensions of the system state vector, is provided in ``_ctrl_dyn`` of
the ``systems`` module. RL is usually understood as a static controller,
i.e., a one which assigns actions directly to outputs. A dynamical
controller does this indirectly, via an internal state as intermediate
link. ynamical controllers can overcome some limitations of static
controllers.
Related literature
==================
`To table of content <#Table-of-content>`__
- `Online actor-critic via stacked Q-learning and Kalman filter for
model estimation <https://arxiv.org/abs/2007.03999>`__
- `More on fusion of model-predictive control and reinforcement
learing <https://arxiv.org/abs/1906.02580>`__
- `More on "JACS", joint actor-critic
(stabilizing) <https://arxiv.org/abs/2006.14034>`__
- `Some comparison of model-predictive control vs. stacked
Q-learning <https://arxiv.org/abs/2108.04802>`__
Closing remarks
===============
`To table of content <#Table-of-content>`__
Please contact `me <mailto:p.osinenko@gmail.com>`__ for any inquiries
and don't forget to give me credit for usage of this code. If you are
interested in stacked Q-learning, kindly read the
`paper <https://arxiv.org/abs/2007.03999>`__.
Original author: P. Osinenko, 2020
Bibtex reference
----------------
::
@misc{rcognita2020,
author = {Pavel Osinenko},
title = {Rcognita: a framework for hybrid agent-enviroment simultion},
howpublished = {\url{https://github.com/AIDynamicAction/rcognita}},
year = {2020}
}