We introduce improved learning and planning algorithms for average-reward MDPs, including 1) the first general proven-convergent off-policy model-free control algorithm without reference states, 2) the first proven-convergent off-policy model-free prediction algorithm, and 3) the first learning algorithms that converge to the actual value function rather than to the value function plus an offset. All of our algorithms are based on using the temporal-difference error rather than the conventional error when updating the estimate of the average reward. Our proof techniques are based on those of Abounadi, Bertsekas, and Borkar (2001). Empirically, we show that the use of the temporal-difference error generally results in faster learning, and that reliance on a reference state generally results in slower learning and risks divergence. All of our learning algorithms are fully online, and all of our planning algorithms are fully incremental.

Human trust in automation plays an essential role in interactions between humans and automation. While a lack of trust can lead to a human's disuse of automation, over-trust can result in a human trusting a faulty autonomous system which could have negative consequences for the human. Therefore, human trust should be calibrated to optimize human-machine interactions with respect to context-specific performance objectives. In this article, we present a probabilistic framework to model and calibrate a human's trust and workload dynamics during his/her interaction with an intelligent decision-aid system. This calibration is achieved by varying the automation's transparency--the amount and utility of information provided to the human. The parameterization of the model is conducted using behavioral data collected through human-subject experiments, and three feedback control policies are experimentally validated and compared against a non-adaptive decision-aid system. The results show that human-automation team performance can be optimized when the transparency is dynamically updated based on the proposed control policy. This framework is a first step toward widespread design and implementation of real-time adaptive automation for use in human-machine interactions. Automation has become prevalent in the everyday lives of humans. However, despite significant technological advancements, human supervision and intervention are still necessary in almost all sectors of automation, ranging from manufacturing and transportation to disaster-management and healthcare [1]. Therefore, we expect that the future will be built around human-agent collectives [2] that will require efficient and successful interaction and coordination between humans and machines. It is well established that to achieve this coordination, human trust in automation plays a central role [3]-[5]. For example, the benefits of automation are lost when humans override automation due to a fundamental lack of trust [3], [5], and accidents may occur due to human mistrust in such systems [6]. Therefore, trust should be appropriately calibrated to avoid disuse or misuse of automation [4].

Make your computer talk, draw graphics, and create an arcade game. Created by Matt Bohn Students also bought Unsupervised Machine Learning Hidden Markov Models in Python Data Science: Supervised Machine Learning in Python Python and Django Full Stack Web Developer Bootcamp The Python Bible Everything You Need to Program in Python Complete Python Developer in 2020: Zero to Mastery Preview this course GET COUPON CODE Description Learn to Code with Simple and Fun Hands On Videos Do you want to learn to code? Maybe you are interested in programming as a career or a hobbyist who wants to create code for your own projects? Or, maybe you're a parent with a student who would love to write code. If so then this is the course you're looking for.

Online Courses Udemy - Software development in Python: A practical approach Learn to build real apps with python NEW Created by Daniel IT English [Auto] Students also bought Data Science: Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning Prerequisites: Linear Regression in Python Unsupervised Machine Learning Hidden Markov Models in Python 2020 Complete Python Bootcamp: From zero to hero in Python Preview this course GET COUPON CODE Description The reason I got into python, I wanted to be a software engineer, I had just built a chat app in PHP and JQuery and a girl asked me if it could run on phone. I responded yes, but I knew that would only be possible using help using non-native means. I wanted native builds, not some complex framework which will only allow me to make a web app whiles I could use the time to study a full fledge programming language. There were others like making a web view app, I didn't like the Idea because there would definetely be setbacks. And I also wanted to be a software engineer or developer, I had built two almost identical CMSs with PHP and I felt I was ready to move into the software development space.

Human action recognition is one of the important fields of computer vision and machine learning. Although various methods have been proposed for 3D action recognition, some of which are basic and some use deep learning, the need of basic methods based on generalized eigenvalue problem is sensed for action recognition. This need is especially sensed because of having similar basic methods in the field of face recognition such as eigenfaces and Fisherfaces. In this paper, we propose Roweisposes which uses Roweis discriminant analysis for generalized subspace learning. This method includes Fisherposes, eigenposes, supervised eigenposes, and double supervised eigenposes as its special cases. Roweisposes is a family of infinite number of action recongition methods which learn a discriminative subspace for embedding the body poses. Experiments on the TST, UTKinect, and UCFKinect datasets verify the effectiveness of the proposed method for action recognition.

Despite the fact that deep reinforcement learning (RL) has surpassed human-level performances in various tasks, it still has several fundamental challenges such as extensive data requirement and lack of interpretability. We investigate the RL problem with non-Markovian reward functions to address such challenges. We enable an RL agent to extract high-level knowledge in the form of finite reward automata, a type of Mealy machines that encode non-Markovian reward functions. The finite reward automata can be converted to deterministic finite state machines, which can be further translated to regular expressions. Thus, this representation is more interpretable than other forms of knowledge representation such as neural networks. We propose an active learning approach that iteratively infers finite reward automata and performs RL (specifically, q-learning) based on the inferred finite reward automata. The inference method is inspired by the L* learning algorithm, and modified in the framework of RL. We maintain two different q-functions, one for answering the membership queries in the L* learning algorithm and the other one for obtaining optimal policies for the inferred finite reward automaton. The experiments show that the proposed approach converges to optimal policies in at most 50% of the training steps as in the two state-of-the-art baselines.

Adaptive thermodynamic systems -- such as a biological organism attempting to gain survival advantage, an autonomous robot performing a functional task, or a motor protein transporting intracellular nutrients -- can improve their performance by effectively modeling the regularities and stochasticity in their environments. Analogously, but in a purely computational realm, machine learning algorithms seek to estimate models that capture predictable structure and identify irrelevant noise in training data by optimizing performance measures, such as a model's log-likelihood of having generated the data. Is there a sense in which these computational models are physically preferred? For adaptive physical systems we introduce the organizing principle that thermodynamic work is the most relevant performance measure of advantageously modeling an environment. Specifically, a physical agent's model determines how much useful work it can harvest from an environment. We show that when such agents maximize work production they also maximize their environmental model's log-likelihood, establishing an equivalence between thermodynamics and learning. In this way, work maximization appears as an organizing principle that underlies learning in adaptive thermodynamic systems.

Reinforcement learning algorithms usually assume that all actions are always available to an agent. However, both people and animals understand the general link between the features of their environment and the actions that are feasible. Gibson (1977) coined the term "affordances" to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents. In this paper, we develop a theory of affordances for agents who learn and plan in Markov Decision Processes. Affordances play a dual role in this case. On one hand, they allow faster planning, by reducing the number of actions available in any given situation. On the other hand, they facilitate more efficient and precise learning of transition models from data, especially when such models require function approximation. We establish these properties through theoretical results as well as illustrative examples. We also propose an approach to learn affordances and use it to estimate transition models that are simpler and generalize better.

Euclidean Markov decision processes are a powerful tool for modeling control problems under uncertainty over continuous domains. Finite state imprecise, Markov decision processes can be used to approximate the behavior of these infinite models. In this paper we address two questions: first, we investigate what kind of approximation guarantees are obtained when the Euclidean process is approximated by finite state approximations induced by increasingly fine partitions of the continuous state space. We show that for cost functions over finite time horizons the approximations become arbitrarily precise. Second, we use imprecise Markov decision process approximations as a tool to analyse and validate cost functions and strategies obtained by reinforcement learning. We find that, on the one hand, our new theoretical results validate basic design choices of a previously proposed reinforcement learning approach. On the other hand, the imprecise Markov decision process approximations reveal some inaccuracies in the learned cost functions.

We introduce a new recurrent agent architecture and associated auxiliary losses which improve reinforcement learning in partially observable tasks requiring long-term memory. We employ a temporal hierarchy, using a slow-ticking recurrent core to allow information to flow more easily over long time spans, and three fast-ticking recurrent cores with connections designed to create an information asymmetry. The \emph{reaction} core incorporates new observations with input from the slow core to produce the agent's policy; the \emph{perception} core accesses only short-term observations and informs the slow core; lastly, the \emph{prediction} core accesses only long-term memory. An auxiliary loss regularizes policies drawn from all three cores against each other, enacting the prior that the policy should be expressible from either recent or long-term memory. We present the resulting \emph{Perception-Prediction-Reaction} (PPR) agent and demonstrate its improved performance over a strong LSTM-agent baseline in DMLab-30, particularly in tasks requiring long-term memory. We further show significant improvements in Capture the Flag, an environment requiring agents to acquire a complicated mixture of skills over long time scales. In a series of ablation experiments, we probe the importance of each component of the PPR agent, establishing that the entire, novel combination is necessary for this intriguing result.