Aquatic organisms involved in predator-prey interactions perform impressive feats of fluid manipulation to enhance their chances of survival [1-8]. Since early studies where prey fish were reported to rapidly accelerate from rest by bending into a C-shape and unfurling their tail [9-12], impulsive locomotion patterns have been the subject of intense investigation. Studying escape strategies of prey fish has led to the discovery of sensing mechanisms [13-15], dedicated neural circuits [16-19], and bio-mechanic principles [20, 21]. From the perspective of hydrodynamics, several studies have sought to understand the C-start escape response and how it imparts momentum to the surrounding fluid [22-27]. However, experiments and observations indicate that swimming escapes can take a variety of forms. For example, after the initial burst from rest, many fish are seen coasting instead of swimming continuously [11, 28, 29]. Furthermore, theoretical [30-32] as well as experimental [33] studies have suggested that intermittent swimming styles, termed burst-coast swimming, can be more efficient than continuous swimming when maximizing distance given a fixed amount of energy. This raises the question of when and why different swimming escape patterns are employed in nature, and which biophysical cost functions they optimize. Given a cost function, reverse engineering methodologies have been employed to identify links to resulting swimming patterns e.g.

Building human-like agent, which aims to learn and think like human intelligence, has long been an important research topic in AI. To train and test human-like agents, we need an environment that imposes the agent to rich multimodal perception and allows comprehensive interactions for the agent, while also easily extensible to develop custom tasks. However, existing approaches do not support comprehensive interaction with the environment or lack variety in modalities. Also, most of the approaches are difficult or even impossible to implement custom tasks. In this paper, we propose a novel VR-based toolkit, VECA, which enables building fruitful virtual environments to train and test human-like agents. In particular, VECA provides a humanoid agent and an environment manager, enabling the agent to receive rich human-like perception and perform comprehensive interactions. To motivate VECA, we also provide 24 interactive tasks, which represent (but are not limited to) four essential aspects in early human development: joint-level locomotion and control, understanding contexts of objects, multimodal learning, and multi-agent learning. To show the usefulness of VECA on training and testing human-like learning agents, we conduct experiments on VECA and show that users can build challenging tasks for engaging human-like algorithms, and the features supported by VECA are critical on training human-like agents.

In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to ridesharing problems. Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, and dynamic pricing are covered. Popular data sets and open simulation environments are also introduced. Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.

Self-supervised representation learning has achieved remarkable success in recent years. By subverting the need for supervised labels, such approaches are able to utilize the numerous unlabeled images that exist on the Internet and in photographic datasets. Yet to build truly intelligent agents, we must construct representation learning algorithms that can learn not only from datasets but also learn from environments. An agent in a natural environment will not typically be fed curated data. Instead, it must explore its environment to acquire the data it will learn from. We propose a framework, curious representation learning (CRL), which jointly learns a reinforcement learning policy and a visual representation model. The policy is trained to maximize the error of the representation learner, and in doing so is incentivized to explore its environment. At the same time, the learned representation becomes stronger and stronger as the policy feeds it ever harder data to learn from. Our learned representations enable promising transfer to downstream navigation tasks, performing better than or comparably to ImageNet pretraining without using any supervision at all. In addition, despite being trained in simulation, our learned representations can obtain interpretable results on real images.

Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT -- a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to understand the semantics of protocol messages and to control the device to reach a given goal, while minimizing the number of interactions. We assume only to know a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb for our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT opens the opportunity to use RL to automatically explore how to interact with IoT protocols with limited information, and paving the road for interoperable systems.

Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences, and thus an agent can extract information about what humans want from the state. Such learning is possible in principle, but requires simulating all possible past trajectories that could have led to the observed state. This is feasible in gridworlds, but how do we scale it to complex tasks? In this work, we show that by combining a learned feature encoder with learned inverse models, we can enable agents to simulate human actions backwards in time to infer what they must have done. The resulting algorithm is able to reproduce a specific skill in MuJoCo environments given a single state sampled from the optimal policy for that skill.

This highlights the complexity of human vision and agency. The next time you go to a supermarket, consider how easily you can find your way through aisles, tell the difference between different products, reach for and pick up different items, place them in your basket or cart, and choose your path in an efficient way. And you're doing all this without access to segmentation and depth maps and by reading items from a crumpled handwritten note in your pocket. Above: Experiments show hybrid AI models that combine reinforcement learning with symbolic planners are better suited to solving the ThreeDWorld Transport Challenge. The TDW-Transport Challenge is in the process of accepting submissions.

The term Delay/Disruption-Tolerant Networks (DTN) invented to describe and cover all types of long-delay, disconnected, intermittently connected networks, where mobility and outages or scheduled contacts may be experienced. This environment is characterized by frequent network partitioning, intermittent connectivity, large or variable delay, asymmetric data rate, and low transmission reliability. There have been routing protocols developed in DTN. However, those routing algorithms are design based upon specific assumptions. The assumption makes existing algorithms suitable for specific environment scenarios. Different routing algorithm uses different relay node selection criteria to select the replication node. Too Frequently forwarding messages can result in excessive packet loss and large buffer and network overhead. On the other hand, less frequent transmission leads to a lower delivery ratio. In DTN there is a trade-off off between delivery ratio and overhead. In this study, we proposed context-adaptive reinforcement learning based routing(CARL-DTN) protocol to determine optimal replicas of the message based on the real-time density. Our routing protocol jointly uses a real-time physical context, social-tie strength, and real-time message context using fuzzy logic in the routing decision. Multi-hop forwarding probability is also considered for the relay node selection by employing Q-Learning algorithm to estimate the encounter probability between nodes and to learn about nodes available in the neighbor by discounting reward. The performance of the proposed protocol is evaluated based on various simulation scenarios. The result shows that the proposed protocol has better performance in terms of message delivery ratio and overhead.

Reward engineering and designing an incentive reward function are non-trivial tasks to train agents in complex environments. Furthermore, an inaccurate reward function may lead to a biased behaviour which is far from an efficient and optimised behaviour. In this paper, we focus on training a single agent to score goals with binary success/failure reward function in Half Field Offense domain. As the major advantage of this research, the agent has no presumption about the environment which means it only follows the original formulation of reinforcement learning agents. The main challenge of using such a reward function is the high sparsity of positive reward signals. To address this problem, we use a simple prediction-based exploration strategy (called Curious Exploration) along with a Return-based Memory Restoration (RMR) technique which tends to remember more valuable memories. The proposed method can be utilized to train agents in environments with fairly complex state and action spaces. Our experimental results show that many recent solutions including our baseline method fail to learn and perform in complex soccer domain. However, the proposed method can converge easily to the nearly optimal behaviour. The video presenting the performance of our trained agent is available at http://bit.ly/HFO_Binary_Reward.

The Sequence Scope is a summary of the most important published research papers, released technology and startup news in the AI ecosystem in the last week. This compendium is part of TheSequence newsletter. Data scientists, scholars, and developers from Microsoft Research, Intel Corporation, Linux Foundation AI, Google, Lockheed Martin, Cardiff University, Mellon College of Science, Warsaw University of Technology, Universitat Politècnica de València and other companies and universities are already subscribed to TheSequence. Reinforcement Learning (RL) has been at the center of some of the most important milestones of the last decade of deep learning. DeepMind's RL-based AlphaGo is considered by many the "Sputnik moment" in artificial intelligence (AI), responsible for sparking an innovation race between the top AI labs in the world.