In recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. Employing simulation-based environments in reinforcement learning enables a priori end-to-end optimization of the control system, provides a virtual testbed for safety-critical control applications, and allows to gain a deep understanding of the control mechanisms. While reinforcement learning has been applied successfully in a number of rather simple flow control benchmarks, a maj...

Publication date June 2024Source Artificial Intelligence, Volume 331Author s Augustin A. Saucan, Subhro Das, Moe Z. Win...

A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciprocation that sustain the commons? Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design an allocation mechanism that endogenously promotes sustainable contributions from human participants to a common pool resou...

Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the real world. A significant challenge hindering the adoption of RL methods in these domains is the non-robustness of conventional algorithms. In this paper, we argue that a fundamental issue contributing to this lack of robustness lies in the focus on the expected value of the return as the sole ``correct'' optimization ob...

Reinforcement learning (RL) with continuous state and action spaces remains one of the most challenging problems within the field. Most current learning methods focus on integral identities such as value functions to derive an optimal strategy for the learning agent. In this paper, we instead study the dual form of the original RL formulation to propose the first differential RL framework that can handle settings with limited training samples and short-length episodes. Our approach introduces Di...

Advances in artificial intelligence AI will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from large su...

Quantum Reinforcement Learning QRL is burgeoning field that merges neuroscience, psychology, and computer science to create more robust systems. The field has seen surge in research activity, particularly in reinforcement learning RL , where insig...

Deep or reinforcement learning (RL) approaches have been adapted as reactive agents to quickly learn and respond with new investment strategies for portfolio management under the highly turbulent financial market environments in recent years. In many cases, due to the very complex correlations among various financial sectors, and the fluctuating trends in different financial markets, a deep or reinforcement learning based agent can be biased in maximising the total returns of the newly formulate...

Simulators are a pervasive tool in reinforcement learning, but most existing algorithms cannot efficiently exploit simulator access -- particularly in high-dimensional domains that require general function approximation. We explore the power of simulators through online reinforcement learning with {local simulator access} (or, local planning), an RL protocol where the agent is allowed to reset to previously observed states and follow their dynamics during training. We use local simulator access ...

L x2019 article explore le Reinforcement Learning RL et ses applications, incluant les jeux AlphaGo, AlphaStar , les voitures autonomes, et x2026 Continue reading on Medium...

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Model-based reinforcement learning (RL), which learns environment model from offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap between the learned and actual environment, conservatism should be incorporated into the algorithm to balance accurate offline data and imprecise model data. The conservatism of current algorithms mostly relies on model uncertainty estimation. However, unc...

This paper presents a novel learning approach for Dubins Traveling Salesman Problems(DTSP) with Neighborhood (DTSPN) to quickly produce a tour of a non-holonomic vehicle passing through neighborhoods of given task points. The method involves two learning phases: initially, a model-free reinforcement learning approach leverages privileged information to distill knowledge from expert trajectories generated by the LinKernighan heuristic (LKH) algorithm. Subsequently, a supervised learning phase tra...

How neural network based AI systems perform under the hood is currently unknown, but the industry is finding ways to live with black box.The post Dealing With AI ML Uncertainty appeared first on Semiconductor Engineering....

Bohemia Interactives free to play extraction shooter Vigor is finally coming to PC after it first launched as an Xbox One exclusive title. The game was ported over to PlayStation and Nintendo Switch in 2020 and will soon be making its way to Steam Ea...

Introduction The Punic Wars, series of three monumental conflicts between ancient Rome and Carthage, stand as pivotal events in shaping the course of Mediterranean history. These wars spanned over century from 264 to 146 BC, and they characterized in...

Mathematics can often feel like an insurmountable challenge, with its complex equations and abstract concepts. From students grappling with algebra to professionals dealing with statistics, math finds way to insert itself into our lives, demanding at...

The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling ASurveyImage byAuthorMy team and Sandi Besen, Tula Masterman, Mason Sawtell, and Alex Chao recently published survey research paper that offers comprehensiv...

Generative AI Landscape in the Mobile App Development Industry The emergence of Generative Artificial Intelligence Gen AI and its intersection with mobile app development has led to remarkable advancements in creativity, personalization, and user e...

Offline reinforcement learning (RL) algorithms are applied to learn performant, well-generalizing policies when provided with a static dataset of interactions. Many recent approaches to offline RL have seen substantial success, but with one key caveat: they demand substantial per-dataset hyperparameter tuning to achieve reported performance, which requires policy rollouts in the environment to evaluate; this can rapidly become cumbersome. Furthermore, substantial tuning requirements can hamper t...