In my previous post, I have explained how AI works at the background. So kindly check that out before jumping into this. Note: This is not an academic textbook/Mathematical explanation of Algorithms. As the name indicates, supervised learning involves machine learning algorithms that learn under the presence of a supervisor. This is similar to a teacher-student scenario.

Autonomous ultrasound (US) acquisition is an important yet challenging task, as it involves interpretation of the highly complex and variable images and their spatial relationships. In this work, we propose a deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans. Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process. We validate our method in a simulation environment built with real-world data collected in the US imaging of the spine. Experimental results demonstrate that our method can perform reproducible US probe navigation towards the standard scan plane with an accuracy of $4.91mm/4.65^\circ$ in the intra-patient setting, and accomplish the task in the intra- and inter-patient settings with a success rate of $92\%$ and $46\%$, respectively. The results also show that the introduction of image quality optimization in our method can effectively improve the navigation performance.

Learning how to adapt and make real-time informed decisions in dynamic and complex environments is a challenging problem. To learn this task, Reinforcement Learning (RL) relies on an agent interacting with an environment and learning through trial and error to maximize the cumulative sum of rewards received by it. In multi-player Monopoly game, players have to make several decisions every turn which involves complex actions, such as making trades. This makes the decision-making harder and thus, introduces a highly complicated task for an RL agent to play and learn its winning strategies. In this paper, we introduce a Hybrid Model-Free Deep RL (DRL) approach that is capable of playing and learning winning strategies of the popular board game, Monopoly. To achieve this, our DRL agent (1) starts its learning process by imitating a rule-based agent (that resembles the human logic) to initialize its policy, (2) learns the successful actions, and improves its policy using DRL. Experimental results demonstrate an intelligent behavior of our proposed agent as it shows high win rates against different types of agent-players.

There has been a paradigm-shift in urban logistic services in the last years; demand for real-time, instant mobility and delivery services grows. This poses new challenges to logistic service providers as the underlying stochastic dynamic vehicle routing problems (SDVRPs) require anticipatory real-time routing actions. Searching the combinatorial action space for efficient routing actions is by itself a complex task of mixed-integer programming (MIP) well-known by the operations research community. This complexity is now multiplied by the challenge of evaluating such actions with respect to their effectiveness given future dynamism and uncertainty, a potentially ideal case for reinforcement learning (RL) well-known by the computer science community. For solving SDVRPs, joint work of both communities is needed, but as we show, essentially non-existing. Both communities focus on their individual strengths leaving potential for improvement. Our survey paper highlights this potential in research originating from both communities. We point out current obstacles in SDVRPs and guide towards joint approaches to overcome them.

Q-learning (QL), a common reinforcement learning algorithm, suffers from over-estimation bias due to the maximization term in the optimal Bellman operator. This bias may lead to sub-optimal behavior. Double-Q-learning tackles this issue by utilizing two estimators, yet results in an under-estimation bias. Similar to over-estimation in Q-learning, in certain scenarios, the under-estimation bias may degrade performance. In this work, we introduce a new bias-reduced algorithm called Ensemble Bootstrapped Q-Learning (EBQL), a natural extension of Double-Q-learning to ensembles. We analyze our method both theoretically and empirically. Theoretically, we prove that EBQL-like updates yield lower MSE when estimating the maximal mean of a set of independent random variables. Empirically, we show that there exist domains where both over and under-estimation result in sub-optimal performance. Finally, We demonstrate the superior performance of a deep RL variant of EBQL over other deep QL algorithms for a suite of ATARI games.

Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments. Though researchers have attempted to use metrics (e.g., perplexity, BLEU) in language generation tasks or some model-based reinforcement learning methods (e.g., self-play evaluation) for automatic evaluation, these methods only show a very weak correlation with the actual human evaluation in practice. To bridge such a gap, we propose a new framework named ENIGMA for estimating human evaluation scores based on recent advances of off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation, making automatic evaluations feasible. More importantly, ENIGMA is model-free and agnostic to the behavior policies for collecting the experience data (see details in Section 2), which significantly alleviates the technical difficulties of modeling complex dialogue environments and human behaviors. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.

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Free Coupon Discount - Cutting-Edge AI: Deep Reinforcement Learning in Python, Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG Highest Rated Created by Lazy Programmer Inc. Preview this Udemy Course GET COUPON CODE Description Welcome to Cutting-Edge AI! This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course. Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks). While both of these have been around for quite some time, it's only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning. The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.

Common patent objection #methodpatentclaims executing steps as which are a set of a predefined sequence of steps used to implement an #algorithm, without disclosing any functional limitations pertaining to enablement of features as claimed in form of method steps. In a world where terms and conditions appear everywhere, we can't help but be suspicious about'catches' that exist within particular clauses or how we might set ourselves up for trouble by possibly agreeing to something. Thereby it is imperative to understand legal language as to how to draft patent application which will withstand the objections raised by patent examiner. Patent simply is a kind of IPRs and in AI one important subject where research has gained momentum is Deep reinforcement modules. Scientists globally are working on Deep reinforcement learning.

Reinforcement learning (RL) is the area of machine learning that is concerned with how software is able to take the right decision.