We revisit the role of instrumental value as a driver of adaptive behavior. In active inference, instrumental or extrinsic value is quantified by the information-theoretic surprisal of a set of observations measuring the extent to which those observations conform to prior beliefs or preferences. That is, an agent is expected to seek the type of evidence that is consistent with its own model of the world. For reinforcement learning tasks, the distribution of preferences replaces the notion of reward. We explore a scenario in which the agent learns this distribution in a self-supervised manner. In particular, we highlight the distinction between observations induced by the environment and those pertaining more directly to the continuity of an agent in time. We evaluate our methodology in a dynamic environment with discrete time and actions. First with a surprisal minimizing model-free agent (in the RL sense) and then expanding to the model-based case to minimize the expected free energy.

Reinforcement learning (RL) algorithms update an agent's parameters according to one of several possible rules, discovered manually through years of research. Automating the discovery of update rules from data could lead to more efficient algorithms, or algorithms that are better adapted to specific environments. Although there have been prior attempts at addressing this significant scientific challenge, it remains an open question whether it is feasible to discover alternatives to fundamental concepts of RL such as value functions and temporal-difference learning. This paper introduces a new meta-learning approach that discovers an entire update rule which includes both 'what to predict' (e.g. value functions) and 'how to learn from it' (e.g. bootstrapping) by interacting with a set of environments. The output of this method is an RL algorithm that we call Learned Policy Gradient (LPG). Empirical results show that our method discovers its own alternative to the concept of value functions. Furthermore it discovers a bootstrapping mechanism to maintain and use its predictions. Surprisingly, when trained solely on toy environments, LPG generalises effectively to complex Atari games and achieves non-trivial performance. This shows the potential to discover general RL algorithms from data.

We consider the problem of batch multi-task reinforcement learning with observed context descriptors, motivated by its application to personalized medical treatment. In particular, we study two general classes of learning algorithms: direct policy learning (DPL), an imitation-learning based approach which learns from expert trajectories, and model-based learning. First, we derive sample complexity bounds for DPL, and then show that model-based learning from expert actions can, even with a finite model class, be impossible. After relaxing the conditions under which the model-based approach is expected to learn by allowing for greater coverage of state-action space, we provide sample complexity bounds for model-based learning with finite model classes, showing that there exist model classes with sample complexity exponential in their statistical complexity. We then derive a sample complexity upper bound for model-based learning based on a measure of concentration of the data distribution. Our results give formal justification for imitation learning over model-based learning in this setting.

Multi-objective task scheduling (MOTS) is the task scheduling while optimizing multiple and possibly contradicting constraints. A challenging extension of this problem occurs when every individual task is a multi-objective optimization problem by itself. While deep reinforcement learning (DRL) has been successfully applied to complex sequential problems, its application to the MOTS domain has been stymied by two challenges. The first challenge is the inability of the DRL algorithm to ensure that every item is processed identically regardless of its position in the queue. The second challenge is the need to manage large queues, which results in large neural architectures and long training times. In this study we present MERLIN, a robust, modular and near-optimal DRL-based approach for multi-objective task scheduling. MERLIN applies a hierarchical approach to the MOTS problem by creating one neural network for the processing of individual tasks and another for the scheduling of the overall queue. In addition to being smaller and with shorted training times, the resulting architecture ensures that an item is processed in the same manner regardless of its position in the queue. Additionally, we present a novel approach for efficiently applying DRL-based solutions on very large queues, and demonstrate how we effectively scale MERLIN to process queue sizes that are larger by orders of magnitude than those on which it was trained. Extensive evaluation on multiple queue sizes show that MERLIN outperforms multiple well-known baselines by a large margin (>22%).

Udemy Coupon - Python For Beginners Part-1, Beginner to Expert Python.Start from the basics and go all the way to creating your own applications and games! New Created by Suraj Nimbalkar English [Auto]00 Students also bought Advanced AI: Deep Reinforcement Learning in Python ayesian Machine Learning in Python: A/B Testing 2020 Complete Python Bootcamp: From Zero to Hero in Python Python and Django Full Stack Web Developer Bootcamp ython A-Z: Python For Data Science With Real Exercises! Learn Python & Ethical Hacking From Scratch Preview this Course GET COUPON CODE Description Learn Python From Scratch I've created thorough, extensive, but easy to follow content which you'll easily understand and absorb. The course starts with the basics, including Python fundamentals, programming, and user interaction. The curriculum is going to be very hands-on as we walk you from start to finish becoming a professional Python developer.

"Artificial Intelligence Project To Help Bring The Power Of The Sun To Earth Is Picked For First U.S. Exascale System" OpenAI's'dangerous' AI text generator is out: People find GPT-2's words'convincing'

Inspired by the work done by Andrychowicz et al. in [7], they modeled an I. INTRODUCTION LSTM as a meta-learner, which helped to train another neural Today, most deep reinforcement learning techniques require network "learner" classifier using a few-shot framework. Unlike models to be trained on a large number of training samples. In common deep learning optimizers such as Momentum, contrast, Model-Agnostic Meta-Learning (MAML) proposed ADAM, and Adagrad, this method is able to train a model by Finn et.

Human-in-the-loop Reinforcement Learning (HRL) aims to integrate human guidance with Reinforcement Learning (RL) algorithms to improve sample efficiency and performance. The usual human guidance in HRL is binary evaluative "good" or "bad" signal for queried states and actions. However, this suffers from the problems of weak supervision and poor efficiency in leveraging human feedback. To address this, we present EXPAND (Explanation Augmented Feedback) which allows for explanatory information to be given as saliency maps from the human in addition to the binary feedback. EXPAND employs a state perturbation approach based on the state salient information to augment the feedback, reducing the number of human feedback signals required. We choose two domains to evaluate this approach, Taxi and Atari-Pong. We demonstrate the effectiveness of our method on three metrics, environment sample efficiency, human feedback sample efficiency, and agent gaze. We show that our method outperforms our baselines. Finally, we present an ablation study to confirm our hypothesis that augmenting binary feedback with state salient information gives a boost in performance.

As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. First, the highway DM problem is formulated, wherein the vehicles can exchange information via wireless networking. Then, two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies in a predefined driving scenario. Finally, the control performance of the derived DM policies in safety and efficiency is analyzed. Furthermore, the inherent differences of the RL algorithms are embodied and discussed in DM strategies.

The problem of optimizing social welfare objectives on multi sided ride hailing platforms such as Uber, Lyft, etc., is challenging, due to misalignment of objectives between drivers, passengers, and the platform itself. An ideal solution aims to minimize the response time for each hyper local passenger ride request, while simultaneously maintaining high demand satisfaction and supply utilization across the entire city. Economists tend to rely on dynamic pricing mechanisms that stifle price sensitive excess demand and resolve the supply demand imbalances emerging in specific neighborhoods. In contrast, computer scientists primarily view it as a demand prediction problem with the goal of preemptively repositioning supply to such neighborhoods using black box coordinated multi agent deep reinforcement learning based approaches. Here, we introduce explainability in the existing supply repositioning approaches by establishing the need for coordination between the drivers at specific locations and times. Explicit need based coordination allows our framework to use a simpler non deep reinforcement learning based approach, thereby enabling it to explain its recommendations ex post. Moreover, it provides envy free recommendations i.e., drivers at the same location and time do not envy one another's future earnings. Our experimental evaluation demonstrates the effectiveness, the robustness, and the generalizability of our framework. Finally, in contrast to previous works, we make available a reinforcement learning environment for end to end reproducibility of our work and to encourage future comparative studies.