Github: mikecb starred google/fleetspeak

from mikecb’s Activity on Github:


A Mathematical Theory of Communication

THE recent development of various methods of modulation such as PCM and PPM which exchange
bandwidth for signal-to-noise ratio has intensified the interest in a general theory of communication. A
basis for such a theory is contained in the important papers of Nyquist1 and Hartley2 on this subject. In the
present paper we will extend the theory to include a number of new factors, in particular the effect of noise
in the channel, and the savings possible due to the statistical structure of the original message and due to the
nature of the final destination of the information.


Design patterns for container-based distributed systems

In the late 1980s and early 1990s, object-oriented pro-
gramming revolutionized software development, popu-
larizing the approach of building of applications as col-
lections of modular components. Today we are seeing
a similar revolution in distributed system development,
with the increasing popularity of microservice archi-
tectures built from containerized software components.
Containers [15] [22] [1] [2] are particularly well-suited
as the fundamental “object” in distributed systems by
virtue of the walls they erect at the container bound-
ary. As this architectural style matures, we are seeing the
emergence of design patterns, much as we did for object-
oriented programs, and for the same reason – thinking in
terms of objects (or containers) abstracts away the low-
level details of code, eventually revealing higher-level
patterns that are common to a variety of applications and
This paper describes three types of design patterns
that we have observed emerging in container-based dis-
tributed systems: single-container patterns for container
management, single-node patterns of closely cooperat-
ing containers, and multi-node patterns for distributed
algorithms. Like object-oriented patterns before them,
these patterns for distributed computation encode best
practices, simplify development, and make the systems
where they are used more reliable.


TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

We introduce TensorFlow Agents, an efficient infrastructure paradigm for
building parallel reinforcement learning algorithms in TensorFlow. We simu-
late multiple environments in parallel, and group them to perform the neural
network computation on a batch rather than individual observations. This
allows the TensorFlow execution engine to parallelize computation, without
the need for manual synchronization. Environments are stepped in separate
Python processes to progress them in parallel without interference of the global
interpreter lock. As part of this project, we introduce BatchPPO, an efficient
implementation of the proximal policy optimization algorithm. By open sourc-
ing TensorFlow Agents, we hope to provide a flexible starting point for future
projects that accelerates future research in the field.