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...

team of roboticists at the University of California, Berkeley, reports that it is possible to train robots to do relatively simple tasks by using sim to real reinforcement learning to train them. In their study, published in the journal Science Robot...

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...

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

Recently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing algorithms to the control of numerical fluid dynamics environments. Although still in its infancy, the field has seen multiple successes in a short time span, and its fast development pace can certainly be partly imparted to the open-source effort that drives the expansion of the community. Yet, this emerging domain st...

Weve all seen videos over the past few years demonstrating how agile humanoid robots have become, running and jumping with ease. Were no longer surprised by this kind of agilityin fact, weve grown to expect it. The problem is, these shiny demos lack ...

In the world of human amp ndash robot coexistence, ensuring safe interactions is crucial. Traditional logic based methods often lack the intuition required for robots, particularly in complex environments where these methods fail to account for all p...

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Can we applied three theory of learning Behaviorism, Cognitivism, and Constructivism in UX area Of course yes But let me tell you first abour behaviorism.Behaviorism theory is developed with conditioning dogs with food. Overall, this theory tell u...

Traditional trajectory planning methods for autonomous vehicles have several limitations. Heuristic and explicit simple rules make trajectory lack generality and complex motion. One of the approaches to resolve the above limitations of traditional trajectory planning methods is trajectory planning using reinforcement learning. However, reinforcement learning suffers from instability of learning and prior works of trajectory planning using reinforcement learning didn't consider the uncertainties....

Veritone has patented computer implemented method for automated control of electrical power production in grid. The system uses reinforcement 8230 The post Veritone gets grant for automated control system for electrical power production optimization...

The effectiveness of traffic light control has been significantly improved by current reinforcement learning based approaches via better cooperation among multiple traffic lights. Code...

Todays bots cant turn against us, but they can cause harm. AI safety aims to train this tech so it will always be honest, harmless and helpful....

SubscribePrevious Issues Generative AI Insights from the Frontlines recent survey of large enterprises reveals significant shift towards in house application development, driven by the rise of foundation models offering accessible APIs. This move aw...

Learn how to use reinforcement learning algorithms and strategies for solving sequential decision making problems.Continue reading on DataDrivenInvestor...

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...

Constitutional AI CAI , pioneered by Anthropic, is an approach to training AI systems that leverages set of principles, akin to constitution, to guide the AI 8217 s behavior. This method prioritizes implementation of human value through these estab...

We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Policy (LLaRP), adapts a pre-trained frozen LLM to take as input text instructions and visual egocentric observations and output actions directly in the environment. Using reinforcement learning, we train LLaRP to see and act solely through environmental interactions. We show that LLaRP is robust to complex paraphrasings...

Attention-based sequential recommendation methods have shown promise in accurately capturing users' evolving interests from their past interactions. Recent research has also explored the integration of reinforcement learning (RL) into these models, in addition to generating superior user representations. By framing sequential recommendation as an RL problem with reward signals, we can develop recommender systems that incorporate direct user feedback in the form of rewards, enhancing personalizat...

Enlarge credit Bill Oxford iStock Getty Images Plus The US AI Safety Institutepart of the National Institute of Standards and Technology NIST has finally announced its leadership team after much speculation.Appointed as head of AI safety is Paul ...