Deterministic

A gradient-based reinforcement learning algorithm to learn deterministic policies for continuous action spacesPhoto by Jonathan Ford on UnsplashThis article introduces Deep Deterministic Policy Gradient (DDPG) — a Reinforcement Learning algorithm suitable for deterministic policies applied in continuous action spaces. By combining the actor-critic paradigm with deep neural networks, continuous action spaces can be tackled without resorting to stochastic policies.Especially for continuous control tasks in which randomness…

Deterministic trends vs stochastic trends, and how to deal with themPhoto by Ali Abdul Rahman on UnsplashDetecting and dealing with the trend is a key step in the modeling of time series.In this article, we’ll:Describe what is the trend of a time series, and its different characteristics;Explore how to detect it;Discuss ways of dealing with trend;Trend as a building block of time seriesAt any given time, a time series can be decomposed into three parts: trend, seasonality, and the remainder.Additive decomposition of a…

Introduction and theoretical explanation of the DDPG algorithm, which has many applications in the field of continuous controlPhoto by Eyosias G on UnsplashThe DDPG algorithm, first presented at ICLR 2016 by Lillicarp et al. , was a significant breakthrough in terms of Deep Reinforcement Learning algorithms for continuous control, because of its improvement over DQN (which only works with discrete actions), and its very good results and ease of implementation (see ).As for the NAF algorithm presented in the previous…

Why we let randomness dictate our action selection in Reinforcement LearningRock-paper-scissors would be a boring affair with deterministic policies If you are used to deterministic decision-making policies (e.g., as in Deep Q-learning), the need for and use of stochastic policies might elude you. After all, deterministic policies offer a convenient state-action mapping π:s ↦ a, ideally even the optimal mapping (that is, if all the Bellman equations are learned to perfection).In contrast, stochastic policies — represented…

Probabilistic Deep LearningThis article belongs to the series “Probabilistic Deep Learning”. This weekly series covers probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e., know what they do not know.This article covers the main differences between Deterministic and Probabilistic deep learning. Deterministic deep learning models are trained to optimize a scalar-valued loss function, while probabilistic deep learning models are trained to optimize a…

Probabilistic deep learningThis article belongs to the series “Probabilistic Deep Learning”. This weekly series covers probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e. know what they do not know.This article will explore the main differences between deterministic and probabilistic regression. In general, deterministic regression is practical when the relationship between the independent and dependent variables is well understood and relatively stable.…