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.

Ex Input:
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either
 discrete or continuous space. Motivated by the project of design Game AI for King of Glory
 (KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous
 hybrid action space. To directly apply existing DLR frameworks, existing approaches
 either approximate the hybrid space by a discrete set or relaxing it into a continuous set, which is
 usually less efficient and robust. In this paper, we propose a parametrized deep Q-network (P-DQN)
 for the hybrid action space without approximation or relaxation. Our algorithm combines DQN and
 DDPG and can be viewed as an extension of the DQN to hybrid actions. The empirical study on the
 game KOG validates the efficiency and effectiveness of our method.

Ex Output:
A DQN and DDPG hybrid algorithm is proposed to deal with the discrete-continuous hybrid action space.


Ex Input:
The conversion of scanned documents to digital forms is performed using an Optical Character Recognition (OCR) software. This work focuses on improving the quality of scanned documents in order to improve the OCR output. We create an end-to-end document enhancement pipeline which takes in a set of noisy documents and produces clean ones. Deep neural network based denoising auto-encoders are trained to improve the OCR quality. We train a blind model that works on different noise levels of scanned text documents. Results are shown for blurring and watermark noise removal from noisy scanned documents.

Ex Output:
We designed and tested a REDNET (ResNet Encoder-Decoder) with 8 skip connections to remove noise from documents, including blurring and watermarks, resulting in a high performance deep network for document image cleanup. 


Ex Input:
How can we teach artificial agents to use human language flexibly to solve problems in a real-world environment? We have one example in nature of agents being able to solve this problem: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop. Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn a language in this way (3). An outstanding goal is finding an algorithm with a suitable ‘language learning prior’ that allows it to learn human language, while minimizing the number of required human interactions.

 In this paper, we propose to learn such a prior in simulation, leveraging the increasing amount of available compute for machine learning experiments (1). We call our approach Learning to Learn to Communicate (L2C). Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations unseen during training, including populations of humans. To show the promise of the L2C framework, we conduct some preliminary experiments in a Lewis signaling game (4), where we show that agents
 trained with L2C are able to learn a simple form of human language (represented by a hand-coded compositional language) in fewer iterations than randomly initialized agents.

Ex Output:

We propose to use meta-learning for more efficient language learning, via a kind of 'domain randomization'.