Reinforcement learning is a form of machine learning that allows an algorithm to interact directly with its environment, learning through punishment …

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

Recent advances in vision-based navigation and exploration have shown impressive capabilities in photorealistic indoor environments. However, these methods still struggle with long-horizon tasks and require large amounts of data to generalize to unseen environments. In this work, we present a novel reinforcement learning approach for multi-object search that combines short-term and long-term reasoning in a single model while avoiding the complexities arising from hierarchical structures. In contrast to existing multi-object search methods that act in granular discrete action spaces, our approach achieves exceptional performance in continuous action spaces. We perform extensive experiments and show that it generalizes to unseen apartment environments with limited data. Furthermore, we demonstrate zero-shot transfer of the learned policies to an office environment in real world experiments.

A wide range of modern artificial intelligence challenges ca n be cast as Reinforcement Learning (RL) problems under partial observability, in which agents learn to make a sequence of decisions despite lacking complete information about the underlying state of system. For example, in robotics the agent has to cope with noisy sensors, occlusions, and unk nown dynamics ( Akkaya et al., 2019), while in imperfect information games the player makes only l ocal observations ( Vinyals et al., 2019; Brown and Sandholm, 2019). Further applications of partially observable RL include autonomous driving ( Levinson et al., 2011), resource allocation ( Bower and Gilbert, 2005), medical diagnostic systems ( Hauskrecht and Fraser, 2000), recommendation ( Li et al., 2010), business management ( De Brito and Van Der Laan, 2009), etc. As such, learning and acting under partial observabi lity has been an important topic in operation research, control, and machine learning. Because of the non-Markovian nature of the observations, le arning and planning in partially observable environments requires an agent to maintain memory and possibly reason about beliefs over the states, all while exploring to collect information about the environment. As such, partial observability can significantly complicate learni ng and planning under uncertainty. While practical RL systems have succeeded in a set of partially obs ervable problems including Poker ( Brown and Sandholm, 2019), Starcraft ( Vinyals et al., 2019) and certain robotic tasks ( Cassandra et al., The author emails are {qinghual, alan.chung,

This projects contains demo video, steps and source codes / tutorial for easiness or reference purpose. This curated list is suitable for beginners and intermediate ML Practitioners. Step 4. Find area using FindContours Firstly, the algorithm have to find where the grids are! Once grids are extracted, for each grid you've to: Cyril Diagne (the creator of this project) has used BASNet for salient object detection and background removal. The accuracy and range of this model are stunning and there are many nice use cases so I packaged it as a micro-service / docker image: Basnet.

Recent studies have shown that episodic reinforcement learning (RL) is not more difficult than contextual bandits, even with a long planning horizon and unknown state transitions. However, these results are limited to either tabular Markov decision processes (MDPs) or computationally inefficient algorithms for linear mixture MDPs. In this paper, we propose the first computationally efficient horizon-free algorithm for linear mixture MDPs, which achieves the optimal $\tilde O(d\sqrt{K} +d^2)$ regret up to logarithmic factors. Our algorithm adapts a weighted least square estimator for the unknown transitional dynamic, where the weight is both \emph{variance-aware} and \emph{uncertainty-aware}. When applying our weighted least square estimator to heterogeneous linear bandits, we can obtain an $\tilde O(d\sqrt{\sum_{k=1}^K \sigma_k^2} +d)$ regret in the first $K$ rounds, where $d$ is the dimension of the context and $\sigma_k^2$ is the variance of the reward in the $k$-th round. This also improves upon the best-known algorithms in this setting when $\sigma_k^2$'s are known.

Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas inspired by elementary Microeconomics. Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer. We show that the emergent production, consumption, and pricing behaviours respond to environmental conditions in the directions predicted by supply and demand shifts in Microeconomics. We also demonstrate settings where the agents' emergent prices for goods vary over space, reflecting the local abundance of goods.

The current work on reinforcement learning (RL) from demonstrations often assumes the demonstrations are samples from an optimal policy, an unrealistic assumption in practice. When demonstrations are generated by sub-optimal policies or have sparse state-action pairs, policy learned from sub-optimal demonstrations may mislead an agent with incorrect or non-local action decisions. We propose a new method called Local Ensemble and Reparameterization with Split and Merge of expert policies (LEARN-SAM) to improve efficiency and make better use of the sub-optimal demonstrations. First, LEARN-SAM employs a new concept, the lambda-function, based on a discrepancy measure between the current state to demonstrated states to "localize" the weights of the expert policies during learning. Second, LEARN-SAM employs a split-and-merge (SAM) mechanism by separating the helpful parts in each expert demonstration and regrouping them into new expert policies to use the demonstrations selectively. Both the lambda-function and SAM mechanism help boost the learning speed. Theoretically, we prove the invariant property of reparameterized policy before and after the SAM mechanism, providing theoretical guarantees for the convergence of the employed policy gradient method. We demonstrate the superiority of the LEARN-SAM method and its robustness with varying demonstration quality and sparsity in six experiments on complex continuous control problems of low to high dimensions, compared to existing methods on RL from demonstration.

Sample efficiency for policy gradient methods is pretty poor. We throw out each batch of data immediately after just one gradient step. This is the most complete Reinforcement Learning course series on Udemy. In it, you will learn to implement some of the most powerful Deep Reinforcement Learning algorithms in Python using PyTorch and PyTorch lightning. You will implement from scratch adaptive algorithms that solve control tasks based on experience.

The idea of robustness is central and critical to modern statistical analysis. However, despite the recent advances of deep neural networks (DNNs), many studies have shown that DNNs are vulnerable to adversarial attacks. Making imperceptible changes to an image can cause DNN models to make the wrong classification with high confidence, such as classifying a benign mole as a malignant tumor and a stop sign as a speed limit sign. The trade-off between robustness and standard accuracy is common for DNN models. In this paper, we introduce sensible adversarial learning and demonstrate the synergistic effect between pursuits of standard natural accuracy and robustness. Specifically, we define a sensible adversary which is useful for learning a robust model while keeping high natural accuracy. We theoretically establish that the Bayes classifier is the most robust multi-class classifier with the 0-1 loss under sensible adversarial learning. We propose a novel and efficient algorithm that trains a robust model using implicit loss truncation. We apply sensible adversarial learning for large-scale image classification to a handwritten digital image dataset called MNIST and an object recognition colored image dataset called CIFAR10. We have performed an extensive comparative study to compare our method with other competitive methods. Our experiments empirically demonstrate that our method is not sensitive to its hyperparameter and does not collapse even with a small model capacity while promoting robustness against various attacks and keeping high natural accuracy.