State-of-the-art approaches to ObjectGoal navigation Prior work has made good progress on this task by rely on reinforcement learning and typically require significant formulating it as a reinforcement learning (RL) problem computational resources and time for learning. We and developing useful representations [20, 60], auxiliary propose Potential functions for ObjectGoal Navigation with tasks [61], data augmentation techniques [37], and improved Interaction-free learning (PONI), a modular approach that reward functions [37]. Despite this progress, end-toend disentangles the skills of'where to look?' for an object and RL incurs high computational cost, has poor sample efficiency, 'how to navigate to (x, y)?'. Our key insight is that'where and tends to generalize poorly to new scenes [7,12, to look?' can be treated purely as a perception problem, 37] since skills like moving without collisions, exploration, and learned without environment interactions. To address and stopping near the object are all learned from scratch this, we propose a network that predicts two complementary purely using RL. Modular navigation methods aim to address potential functions conditioned on a semantic map and uses these issues by disentangling'where to look for an object?' them to decide where to look for an unseen object. We train and'how to navigate to (x, y)?' [12,36]. These methods the potential function network using supervised learning on have emerged as strong competitors to end-to-end RL a passive dataset of top-down semantic maps, and integrate with good sample efficiency, better generalization to new it into a modular framework to perform ObjectGoal navigation.

Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to having many choices. Hierarchical reinforcement learning methods aim to solve the first problem, by providing shortcuts that skip over multiple time steps. To cope with the breadth, it is desirable to restrict the agent's attention at each step to a reasonable number of possible choices. The concept of affordances (Gibson, 1977) suggests that only certain actions are feasible in certain states. In this work, we model "affordances" through an attention mechanism that limits the available choices of temporally extended options. We present an online, model-free algorithm to learn affordances that can be used to further learn subgoal options. We investigate the role of hard versus soft attention in training data collection, abstract value learning in long-horizon tasks, and handling a growing number of choices. We identify and empirically illustrate the settings in which the paradox of choice arises, i.e. when having fewer but more meaningful choices improves the learning speed and performance of a reinforcement learning agent.

ABSTRACT Intravascular ultrasound (IVUS) offers a unique perspective in the treatment of vascular diseases by creating a sequence of ultrasound-slices acquired from within the vessel. However, unlike conventional hand-held ultrasound, the thin catheter only provides room for a small number of physical channels for signal transfer from a transducer-array at the tip. For continued improvement of image quality and frame rate, we present the use of deep reinforcement learning to deal with the current physical information bottleneck. V aluable inspiration has come from the field of magnetic resonance imaging (MRI), where learned acquisition schemes have brought significant acceleration in image acquisition at competing image quality. To efficiently accelerate IVUS imaging, we propose a framework that utilizes deep reinforcement learning for an optimal adaptive acquisition policy on a per-frame basis enabled by actor-critic methods and Gumbel top-K sampling. Index T erms-- Deep reinforcement learning, intravascu-lar ultrasound, compressed sensing 1. INTRODUCTION Minimally invasive vascular interventions are increasingly guided by phased array intravascular imaging.

We study the off-policy evaluation (OPE) problem in an infinite-horizon Markov decision process with continuous states and actions. We recast the $Q$-function estimation into a special form of the nonparametric instrumental variables (NPIV) estimation problem. We first show that under one mild condition the NPIV formulation of $Q$-function estimation is well-posed in the sense of $L^2$-measure of ill-posedness with respect to the data generating distribution, bypassing a strong assumption on the discount factor $\gamma$ imposed in the recent literature for obtaining the $L^2$ convergence rates of various $Q$-function estimators. Thanks to this new well-posed property, we derive the first minimax lower bounds for the convergence rates of nonparametric estimation of $Q$-function and its derivatives in both sup-norm and $L^2$-norm, which are shown to be the same as those for the classical nonparametric regression (Stone, 1982). We then propose a sieve two-stage least squares estimator and establish its rate-optimality in both norms under some mild conditions. Our general results on the well-posedness and the minimax lower bounds are of independent interest to study not only other nonparametric estimators for $Q$-function but also efficient estimation on the value of any target policy in off-policy settings.

With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicating that competitive sparse representations should be learnable, non-prior, non-truncated and explicit when compared with current sparse representation methods. Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA. In this paper, we propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization and provide convergence analysis of our proposed algorithm. Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain Car, Acrobot, CartPole and Puddle World tasks.

In 9 hours, Google's AlphaZero went from only knowing the rules of chess to beating the best models in the world. Chess has been studied by humans for over 1000 years, yet a reinforcement learning model was able to further our knowledge of the game in a negligible amount of time, using no prior knowledge aside from the game rules. No other machine learning field allows for such progress in this problem. Today, similar models by Google are being used in a wide variety of fields like predicting and detecting early signs of life-changing illnesses, improving text-to-speech systems, and more. Machine learning can be divided into 3 main paradigms.

In 9 hours, Google's AlphaZero went from only knowing the rules of chess to beating the best models in the world. Chess has been studied by humans for over 1000 years, yet a reinforcement learning model was able to further our knowledge of the game in a negligible amount of time, using no prior knowledge aside from the game rules. No other machine learning field allows for such progress in this problem. Today, similar models by Google are being used in a wide variety of fields like predicting and detecting early signs of life-changing illnesses, improving text-to-speech systems, and more. Machine learning can be divided into 3 main paradigms.

Model-free and model-based reinforcement learning are two ends of a spectrum. Learning a good policy without a dynamic model can be prohibitively expensive. Learning the dynamic model of a system can reduce the cost of learning the policy, but it can also introduce bias if it is not accurate. We propose a middle ground where instead of the transition model, the sensitivity of the trajectories with respect to the perturbation of the parameters is learned. This allows us to predict the local behavior of the physical system around a set of nominal policies without knowing the actual model.

Recent advances in reinforcement learning (RL) have increased complexity which, especially for deep RL, has brought forth challenges related to explainability [1]. The opacity of state-of-the-art RL algorithms has led to model developers having a limited understanding of their agents' policies and no influence over learned strategies [2]. While concerns surrounding explainability have been noted for AI in general, it is only more recently that attempts have been made to explain RL systems [3, 1, 4, 5]. These attempts have resulted in a wide suite of methods requiring various degrees of expert knowledge, either about the state-action domain or about the specific RL algorithm. Further, they typically rely on post-hoc analysis of learned policies which give only observational assurances of agents' behaviour. We instead propose an intrinsic method of regularizing agents' actions based on a given prior. While current methods for RL regularization aim to improve training performance - e.g., by maximizing the entropy of the action distribution [6], or by minimising the distance to a prior sub-optimal state-action distribution [7] - our aim is the characterization of our agents' behaviours. We also extend the current regularization techniques to accommodate multi-agent systems which allows intrinsic characterization of individual agents. We provide a formal argument for the rationale of our method and demonstrate its efficacy in a toy problem where agents learn to navigate to a destination on a grid by performing, e.g., only right turns (under the premise that right turns are

With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence with less energy consumption. It provides a promising energy-efficient way for realistic control tasks by combing SNNs and deep reinforcement learning (RL). There are only a few existing SNN-based RL methods at present. Most of them either lack generalization ability or employ Artificial Neural Networks (ANNs) to estimate value function in training. The former needs to tune numerous hyper-parameters for each scenario, and the latter limits the application of different types of RL algorithm and ignores the large energy consumption in training. To develop a robust spike-based RL method, we draw inspiration from non-spiking interneurons found in insects and propose the deep spiking Q-network (DSQN), using the membrane voltage of non-spiking neurons as the representation of Q-value, which can directly learn robust policies from high-dimensional sensory inputs using end-to-end RL. Experiments conducted on 17 Atari games demonstrate the effectiveness of DSQN by outperforming the ANN-based deep Q-network (DQN) in most games. Moreover, the experimental results show superior learning stability and robustness to adversarial attacks of DSQN.