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Maksis
Maksis

Maksis

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From Simple Reinforcement Learning with Tensorflow: Part 2 - Policy-based Agents by Arthur Juliani

To be a little more formal, we can define a Markov Decision Process as follows. An MDP consists of a set of all possible states S from which our agent at any time will experience s. A set of all possible actions A from which our agent at any time will take action a. Given a state action pair (s, a), the transition probability to a new state s’ is defined by T(s, a), and the reward r is given by R(s, a). As such, at any time in an MDP, an agent is given a state s, takes action a, and receives new state s’ and reward r.

From RESTful API Best Practices and Common Pitfalls by Spencer Schneidenbach

Data should always be assumed to be bad until it’s been through some kind of validation process. Make no assumptions about the data you’re receiving — someone, somewhere will likely send you a re…

From How JavaScript works: inside the V8 engine + 5 tips on how to write optimized code by Alexander Zlatkov

How to write optimized JavaScript

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Simple Reinforcement Learning with Tensorflow Part 1.5: Contextual Bandits

Arthur Juliani

Time-series data: Why (and how) to use a relational database instead of NoSQL

Mike Freedman

A Comparison of Time Series Databases and Netsil’s Use of Druid

Netsil