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.

[Q]: Reinforcement learning (RL) methods achieved major advances in multiple tasks surpassing human performance. However, most of RL strategies show a certain degree of weakness and may become computationally intractable when dealing with high-dimensional and non-stationary environments. In this paper, we build a meta-reinforcement learning (MRL) method embedding an adaptive neural network (NN) controller for efficient policy iteration in changing task conditions. Our main goal is to extend RL application to the challenging task of urban autonomous driving in CARLA simulator.
[A]: A meta-reinforcement learning approach embedding a neural network controller applied to autonomous driving with Carla simulator.

[Q]: Federated learning, where a global model is trained by iterative parameter averaging of locally-computed updates, is a promising approach for distributed training of deep networks; it provides high communication-efficiency and privacy-preservability, which allows to fit well into decentralized data environments, e.g., mobile-cloud ecosystems. However, despite the advantages, the federated learning-based methods still have a challenge in dealing with non-IID training data of local devices (i.e., learners). In this regard, we study the effects of a variety of hyperparametric conditions under the non-IID environments, to answer important concerns in practical implementations: (i) We first investigate parameter divergence of local updates to explain performance degradation from non-IID data. The origin of the parameter divergence is also found both empirically and theoretically. (ii) We then revisit the effects of optimizers, network depth/width, and regularization techniques; our observations show that the well-known advantages of the hyperparameter optimization strategies could rather yield diminishing returns with non-IID data. (iii) We finally provide the reasons of the failure cases in a categorized way, mainly based on metrics of the parameter divergence.
[A]: We investigate the internal reasons of our observations, the diminishing effects of the well-known hyperparameter optimization methods on federated learning from decentralized non-IID data.

[Q]: The weak contraction mapping is a self mapping that the range is always a subset of the domain, which admits a unique fixed-point. The iteration of weak contraction mapping is a Cauchy sequence that yields the unique fixed-point. A gradient-free optimization method as an application of weak contraction mapping is proposed to achieve global minimum convergence. The optimization method is robust to local minima and initial point position.
[A]:
A gradient-free method is proposed for non-convex optimization problem