Offline reinforcement learning aims to train a policy on a pre-recorded and fixed dataset without any additional environment interactions. There are two major challenges in this setting: (1) extrapolation error caused by approximating the value of state-action pairs not well-covered by the training data and (2) distributional shift between behavior and inference policies. One way to tackle these problems is to induce conservatism - i.e., keeping the learned policies closer to the behavioral ones. To achieve this, we build upon recent works on learning policies in latent action spaces and use a special form of Normalizing Flows for constructing a generative model, which we use as a conservative action encoder. This Normalizing Flows action encoder is pre-trained in a supervised manner on the offline dataset, and then an additional policy model - controller in the latent space - is trained via reinforcement learning. This approach avoids querying actions outside of the training dataset and therefore does not require additional regularization for out-of-dataset actions. We evaluate our method on various locomotion and navigation tasks, demonstrating that our approach outperforms recently proposed algorithms with generative action models on a large portion of datasets.

Reinforcement Learning (RL; Sutton and Barto, 2018; Mannor et al., 2022) studies online decision making problems in which an agent learns through experience within a dynamic environment, with the goal to minimize a loss function associated with the agent-environment interaction. Modern applications of RL such as robotics(Schulman et al., 2015; Lillicrap et al., 2015; Akkaya et al., 2019), game playing (Mnih et al., 2013; Silver et al., 2018) and autonomous driving (Kiran et al., 2021), almost invariably consist of large scale environments where function approximation techniques are necessary to allow the agent to generalize across different states. Furthermore, some form of agent robustness is usually required to cope with environment irregularities that cannot be faithfully represented by stochasticity assumptions (see e.g., Dulac-Arnold et al., 2021). Theoretical foundations for RL with function approximation (e.g., Jiang et al., 2017; Yang and Wang, 2019; Jin et al., 2020b; Agarwal et al., 2020) have been steadily coming into fruition.

There are many frameworks for deep generative modeling, each often presented with their own specific training algorithms and inference methods. Here, we demonstrate the connections between existing deep generative models and the recently introduced GFlowNet framework, a probabilistic inference machine which treats sampling as a decision-making process. This analysis sheds light on their overlapping traits and provides a unifying viewpoint through the lens of learning with Markovian trajectories. Our framework provides a means for unifying training and inference algorithms, and provides a route to shine a unifying light over many generative models. Beyond this, we provide a practical and experimentally verified recipe for improving generative modeling with insights from the GFlowNet perspective.

Become a high qualified data scientist by taking these 10 best data science courses on Udemy. Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! Created by Lazy Programmer Inc. Learn how to apply probability and statistics to real data science and business applications! Created by Lazy Programmer Inc. Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

Originally published on Towards AI. Our Learn AI Together Discord community has grown to 35,000 members and we are excited to see the engagement in our new AI Technical Questions forum format where members of our community and team are there to try to help with any AI questions, theory or ops. Building on this we have several exciting new features in the pipeline for the Community this year starting with Community Events. Given the success of the graduate seminar on Neural Networks Architectures that Pablo Duboue (DrDub) taught last year in Argentina, he has decided to reiterate the seminar this year, this time in English in a 9 part series through the Towards AI Discord server. We are excited to host it and hope you will join us and learn with us! Add our Google calendar to see all our free AI events!

Following two action-packed days of workshops, conferences, paper presentations, and tech talks, Machine Learning Developers Summit 2023 concluded by awarding 40 dynamic data scientists with the 40 Under 40 Data Scientists award. Aakash is a seasoned analytics leader with 15 years experience and has been instrumental in driving data and insight-led transformations. Over his career, he has worked closely with biz functions to drive revenue and achieve aggressive market growth by leveraging more than 50 analytical approaches. He also has experience in launching AI and tech-based solutions like Omni Channel Attribution, Customer Segmentation, Customer-360, Supply Chain Efficiency, Workforce Management and more at telecom, media, FMCG, retail, and ecommerce industries. Abhilash Surendran is assistant vice president, analytics, and data science at Merkle, leading the analytics practise for their high-tech portfolio. He comes with 15 years of experience in advanced analytics, data science, data visualisation and consulting.

PyTorch is a powerful Python library for building deep learning models. It provides everything you need to define and train a neural network and use it for inference. You don't need to write a lot of code to get all these done. In this pose, you will discover how to create your first deep learning neural network model in Python using PyTorch. Develop Your First Neural Network with PyTorch, Step-by-Step Photo by drown_ in_city.

The recent global pandemic further amplified the impact of online education as an effective alternative that could overcome physical distancing restrictions imposed on students and teaching staff in schools and university campuses. Nevertheless, one of the significant challenges that need to be addressed in online education systems is the ability to effectively trace a student's learning progress, similar to what a human teacher would do in the classroom. Human teachers rely on their intuition and experience to estimate a student's knowledge state and tailor the learning process accordingly. Acquiring such ability would enable online education systems to archive many vital education objectives, including customized curriculum generation, learning materials recommendation, exercise recommendation, automatic evaluation, or learning feedback generation. Achieving such objectives would facilitate automating the teaching process and pave the way for transforming the current online education systems into Intelligent Tutoring Systems (ITS). An ITS not only automates the teaching procedure using computer systems (e.g., web applications) but also handles supporting tasks such as customizing the learning experience and providing guidance and feedback to the students [1]. The Knowledge Tracing (KT) problem formulates the challenge of tracing a student's knowledge state based on their exercise answering history [2, 3]. In particular, the exercise answering history could be represented as a sequence of question-answer pairs, and the task of a solving computational model would be to predict the likelihood of correctly answering the following questions. Figure 1 depicts a probabilistic graphical model for a KT scenario.

This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pre-trained language models (e.g., GPT2) using the limited training instances to produce new synthetic data. Consequently, they have trivial task-specific knowledge and are limited to yielding low-quality synthetic data. To combat this issue, we propose Knowledge Mixture Data Augmentation Model (KnowDA) which is an Seq2Seq language model pre-trained on a mixture of diverse NLP tasks under a novel framework of Knowledge Mixture Training (KoMT). The goal of KoMT is to condense diverse NLP task-specific knowledge into the single KnowDA model (i.e., all-in-one) such that KnowDA could utilize these knowledge to quickly grasp the inherent synthesis law of the target task through limited training instances. Specifically, KoMT reformulates input examples from various heterogeneous NLP tasks into a unified text-to-text format, and employs denoising training objectives in different granularity to learn to reconstruct partial or complete samples. To the best of our knowledge, we are the first attempt to apply 100+ NLP multi-task training for data augmentation. Extensive experiments show that i) the synthetic data produced by KnowDA successfully improves performance of the strong pre-trained language models (i.e., Bert, ALBert and Deberta) by a large margin on the low-resource NLP benchmark FewGLUE, CoNLL'03 and WikiAnn; ii) KnowDA successfully transfers the task knowledge to NLP tasks whose types are seen and unseen in KoMT.

Despite rapid advancements in lifelong learning (LLL) research, a large body of research mainly focuses on improving the performance in the existing \textit{static} continual learning (CL) setups. These methods lack the ability to succeed in a rapidly changing \textit{dynamic} environment, where an AI agent needs to quickly learn new instances in a `single pass' from the non-i.i.d (also possibly temporally contiguous/coherent) data streams without suffering from catastrophic forgetting. For practical applicability, we propose a novel lifelong learning approach, which is streaming, i.e., a single input sample arrives in each time step, single pass, class-incremental, and subject to be evaluated at any moment. To address this challenging setup and various evaluation protocols, we propose a Bayesian framework, that enables fast parameter update, given a single training example, and enables any-time inference. We additionally propose an implicit regularizer in the form of snap-shot self-distillation, which effectively minimizes the forgetting further. We further propose an effective method that efficiently selects a subset of samples for online memory rehearsal and employs a new replay buffer management scheme that significantly boosts the overall performance. Our empirical evaluations and ablations demonstrate that the proposed method outperforms the prior works by large margins.