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.

Natural language is hierarchically structured: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM architecture allows different neurons to track information at different time scales, it does not have an explicit bias towards modeling a hierarchy of constituents. This paper proposes to add such inductive bias by ordering the neurons; a vector of master input and forget gates ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. Our novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.
We introduce a new inductive bias that integrates tree structures in recurrent neural networks.

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

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.

We propose a hybrid model-based & model-free approach using semantic information to improve DRL generalization in man-made environments.