TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward - making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory - which incorporates rich information about environment dynamics. This allows us to overcome the known "couch-potato" issues of prior work - when the agent finds a way to instantly gratify itself by exploiting actions which lead to hardly predictable consequences. We test our approach in visually rich 3D environments in ViZDoom, DMLab and MuJoCo. In navigational tasks from ViZDoom and DMLab, our agent outperforms the state-of-the-art curiosity method ICM. In MuJoCo, an ant equipped with our curiosity module learns locomotion out of the first-person-view curiosity only. The code is available at https://github.com/google-research/episodic-curiosity/.

SOLUTION: We propose a novel model of curiosity based on episodic memory and the ideas of reachability which allows us to overcome the known "couch-potato" issues of prior work.

PROBLEM: Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data. In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods. We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison. We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.

SOLUTION: We pose that generative models' likelihoods are excessively influenced by the input's complexity, and propose a way to compensate it when detecting out-of-distribution inputs

PROBLEM: Over the passage of time Unmanned Autonomous Vehicles (UAVs), especially
 Autonomous flying drones grabbed a lot of attention in Artificial Intelligence.
 Since electronic technology is getting smaller, cheaper and more efficient, huge
 advancement in the study of UAVs has been observed recently. From monitoring
 floods, discerning the spread of algae in water bodies to detecting forest trail, their
 application is far and wide. Our work is mainly focused on autonomous flying
 drones where we establish a case study towards efficiency, robustness and accuracy
 of UAVs where we showed our results well supported through experiments.
 We provide details of the software and hardware architecture used in the study. We
 further discuss about our implementation algorithms and present experiments that
 provide a comparison between three different state-of-the-art algorithms namely
 TrailNet, InceptionResnet and MobileNet in terms of accuracy, robustness, power
 consumption and inference time. In our study, we have shown that MobileNet has
 produced better results with very less computational requirement and power consumption.
 We have also reported the challenges we have faced during our work
 as well as a brief discussion on our future work to improve safety features and
 performance.

SOLUTION:
case study on optimal deep learning model for UAVs