We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components. Assuming the factorization is known, we propose two model-based algorithms. The first one achieves minimax optimal regret guarantees for a rich class of factored structures, while the second one enjoys better computational complexity with a slightly worse regret. A key new ingredient of our algorithms is the design of a bonus term to guide exploration. We complement our algorithms by presenting several structure-dependent lower bounds on regret for FMDPs that reveal the difficulty hiding in the intricacy of the structures.

Please enjoy'Severance' star Adam Scott reacting to weird fan art of himself Mashable Good Connection Tech Science Life Social Good Entertainment Deals Shopping Games Search Cancel * * Search Result Good Connection Tech Apps & Software Artificial Intelligence Cybersecurity Cryptocurrency Mobile Smart Home Social Media Tech Industry Transportation All Tech Science Space Climate Change Environment All Science Life Digital Culture Family & Parenting Health & Wellness Sex, Dating & Relationships Sleep Careers Mental Health All Life Social Good Activism Gender LGBTQ Racial Justice Sustainability Politics All Social Good Entertainment Games Movies Podcasts TV Shows Watch Guides All Entertainment SHOP THE BEST Laptops Budget Laptops Dating Apps Sexting Apps Hookup Apps VPNs Robot Vaccuums Robot Vaccum & Mop Headphones Speakers Kindles Gift Guides Mashable Choice Mashable Selects All Sex, Dating & Relationships All Laptops All Headphones All Robot Vacuums All VPN All Shopping Games Product Reviews Adult Friend Finder Bumble Premium Tinder Platinum Kindle Paperwhite PS5 vs PS5 Slim All Reviews All Shopping Deals Newsletters VIDEOS Mashable Shows All Videos Home Entertainment TV Shows Please enjoy'Severance' star Adam Scott reacting to weird fan art of himself "That's the most realistic of all." By Sam Haysom Sam Haysom Sam Haysom is the Deputy UK Editor for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. Read Full Bio on March 21, 2025 Share on Facebook Share on Twitter Share on Flipboard Watch Next'Severance' star Tramell Tillman defends his Swedish pronunciation to Stephen Colbert Severance Season 1 Recap: Everything you need to remember before watching season 2 1:11 Ben Stiller explains why Barack Obama turned down a'Severance' Season 2 cameo'Old Guy' stars Lucy Liu and Christoph Waltz debate what makes something art 5:25 You know you've got a hit show when internet strangers are pumping out fan art of your character, and Adam Scott's starring role onSeverance has inspired it in spades. Appearing on Jimmy Kimmel Live! to discuss the Season 2 finale, Scott was presented with a variety of artistic offerings depicting his character in various, deeply unflattering forms.

Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks have proved to be a recipe for success, current methods are still lacking on two fronts: (a) data-efficiency of learning and (b) generalization to new environments. To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms. We perform the first extensive study of general data augmentations for RL on both pixel-based and state-based inputs, and introduce two new data augmentations - random translate and random amplitude scale. We show that augmentations such as random translate, crop, color jitter, patch cutout, random convolutions, and amplitude scale can enable simple RL algorithms to outperform complex state-of-the-art methods across common benchmarks. RAD sets a new state-of-the-art in terms of data-efficiency and final performance on the DeepMind Control Suite benchmark for pixel-based control as well as OpenAI Gym benchmark for state-based control. We further demonstrate that RAD significantly improves test-time generalization over existing methods on several OpenAI ProcGen benchmarks.

Sections S3 to S5 contain the proofs for all results stated in the matching sections from the main article. References to contents in the supplementary material are prefixed with an "S", e.g., Lemma S3.20, eq. References to contents in the main article do not have any prefix, e.g., Theorem 1.3 and Section 1.4. We introduce notations in addition to those already defined in in the main article's Section 1.3. L always denotes the WW-hinge loss (Definition 1.4) and l always denotes the ordered partition loss (Definition 2.2).