instruction:
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.
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:
We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our key innovation is the design of a linear model that incorporates both gradient features and the activation of the network. We show that our model provides a local linear approximation to a underlying deep model, and discuss important theoretical insight. Moreover, we present an efficient algorithm for the training and inference of our model without computing the actual gradients. Our method is evaluated across a number of representation learning tasks on several datasets and using different network architectures. We demonstrate strong results in all settings. And our results are well-aligned with our theoretical insight.
answer:
Given a pre-trained model, we explored the per-sample gradients of the model parameters relative to a task-specific loss, and constructed a linear model that combines gradients of model parameters and the activation of the model.

question:
Distributed optimization is essential for training large models on large datasets. Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers. Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates. Inspired by the BMUF method of Chen & Huo (2016), we propose a slow momentum (SloMo) framework, where workers periodically synchronize and perform a momentum update, after multiple iterations of a base optimization algorithm. Experiments on image classification and machine translation tasks demonstrate that SloMo consistently yields improvements in optimization and generalization performance relative to the base optimizer, even when the additional overhead is amortized over many updates so that the SloMo runtime is on par with that of the base optimizer. We provide theoretical convergence guarantees showing that SloMo converges to a stationary point of smooth non-convex losses. Since BMUF is a particular instance of the SloMo framework, our results also correspond to the first theoretical convergence guarantees for BMUF.
answer:
SlowMo improves the optimization and generalization performance of communication-efficient decentralized algorithms without sacrificing speed.