Reinforcement learning studies sequential decision making problems where a learning agent repeatedly interacts with an environment and aims to improve her strategy over time based on the received feedback. One of the most fundamental tradeoffs in reinforcement learning theory is the exploration vs. exploitation tradeoff, that arises whenever the learner observes only partial feedback after each of her decisions, thus having to balance between exploring new strategies and exploiting those that are already known to perform well. The most basic and well-studied form of partial feedback is the so-called "bandit" feedback, where the learner only observes the cost of her chosen action on each decision round, while obtaining no information about the performance of other actions. Traditionally, the environment dynamics in reinforcement learning are modeled as a Markov Decision Process (MDP) with a finite number of possible states and actions. The MDP model has been studied and analyzed in numerous different settings and under various assumptions on the transition parameters, the nature of the reward functions, and the feedback model. Recently, a particular focus has been given to continuous state-action MDPs, and in particular, to a specific family of models in classic control where the state transition function is linear.

We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved ones. The availability of solutions to related problems poses a fundamental trade-off: whether to seek policies that are expected to achieve high (yet sub-optimal) performance in the new task immediately or whether to seek information to quickly identify an optimal solution, potentially at the cost of poor initial behavior. In this work, we focus on the second objective when the agent has access to a generative model of state-action pairs. First, given a set of solved tasks containing an approximation of the target one, we design an algorithm that quickly identifies an accurate solution by seeking the state-action pairs that are most informative for this purpose. We derive PAC bounds on its sample complexity which clearly demonstrate the benefits of using this kind of prior knowledge. Then, we show how to learn these approximate tasks sequentially by reducing our transfer setting to a hidden Markov model and employing spectral methods to recover its parameters. Finally, we empirically verify our theoretical findings in simple simulated domains.

Online Courses Udemy - Deployment of Machine Learning Models Build Machine Learning Model APIs Created by Soledad Galli, Christopher Samiullah English [Auto] Students also bought Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Artificial Intelligence: Reinforcement Learning in Python Unsupervised Machine Learning Hidden Markov Models in Python Deep Learning: Recurrent Neural Networks in Python Preview this course GET COUPON CODE Description Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.

Partially-Observable Markov Decision Processes (POMDPs) are a well-known formal model for planning scenarios where agents operate under limited information about their environment. In safety-critical domains, the agent must adhere to a policy satisfying certain behavioral constraints. We study the problem of synthesizing policies that almost-surely reach some goal state while a set of bad states is never visited. In particular, we present an iterative symbolic approach that computes a winning region, that is, a set of system configurations such that all policies that stay within this set are guaranteed to satisfy the constraints. The approach generalizes and improves previous work in terms of scalability and efficacy, as demonstrated in the empirical evaluation. Additionally, we show the applicability to safe exploration by restricting agent behavior to these winning regions.

Markov decision processes are the model to reason about systems involving nondeterministic choice and probabilistic branching. They have widespread usage in planning and scheduling, robotics, and formal methods. In the latter, the key verification question is whether for any policy, i.e., for any resolution of the nondeterminism, the probability to reach the bad states is below a threshold [3]. The verification question may be efficiently analysed using a variety of techniques such as linear programming, value iteration, or policy iteration, readily available in mature tools such as Storm [15], Prism [22] and Modest [13]. However, those verification results are often overly pessimistic.

We consider reinforcement learning (RL) in episodic MDPs with adversarial full-information reward feedback and unknown fixed transition kernels. We propose two model-free policy optimization algorithms, POWER and POWER++, and establish guarantees for their dynamic regret. Compared with the classical notion of static regret, dynamic regret is a stronger notion as it explicitly accounts for the non-stationarity of environments. The dynamic regret attained by the proposed algorithms interpolates between different regimes of non-stationarity, and moreover satisfies a notion of adaptive (near-)optimality, in the sense that it matches the (near-)optimal static regret under slow-changing environments. The dynamic regret bound features two components, one arising from exploration, which deals with the uncertainty of transition kernels, and the other arising from adaptation, which deals with non-stationary environments. Specifically, we show that POWER++ improves over POWER on the second component of the dynamic regret by actively adapting to non-stationarity through prediction. To the best of our knowledge, our work is the first dynamic regret analysis of model-free RL algorithms in non-stationary environments.

Many non-trivial sequential decision-making problems are efficiently solved by relying on Bellman's optimality principle, i.e., exploiting the fact that sub-problems are nested recursively within the original problem. Here we show how it can apply to (infinite horizon) 2-player zero-sum partially observable stochastic games (zs-POSGs) by (i) taking a central planner's viewpoint, which can only reason on a sufficient statistic called occupancy state, and (ii) turning such problems into zero-sum occupancy Markov games (zs-OMGs). Then, exploiting the Lipschitz-continuity of the value function in occupancy space, one can derive a version of the HSVI algorithm (Heuristic Search Value Iteration) that provably finds an $\epsilon$-Nash equilibrium in finite time.

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.