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.
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Question: 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.

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


Question: Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
 Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
 Furthermore, social networks can be extracted from email corpora, tweets, or social media. 
 When it comes to visualising these large corpora, either the textual content or the network graph are used.

 In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.
 To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.
 We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.

Answer: Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships.


Question: Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.  We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of the environment.The nodes of a world graph are important waypoint states and edges represent feasible traversals between them .  Our framework has two learning phases : 1) identifying world graph nodes and edges by training a binary recurrent variational auto-encoder (VAE) on trajectory data and 2) a hierarchical RL framework that leverages structural and connectivity knowledge from the learned world graph to bias exploration towards task-relevant waypoints and regions. We show that our approach significantly accelerates RL on a suite of challenging 2D grid world tasks: compared to baselines, world graph integration doubles achieved rewards on simpler tasks, e.g. MultiGoal, and manages to solve more challenging tasks, e.g. Door-Key, where baselines fail.

Answer:
We learn a task-agnostic world graph abstraction of the environment and show how using it for structured exploration can significantly accelerate downstream task-specific RL.