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.

Example Input: Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether modern deep reinforcement learning can be used to train agents to perform causal reasoning. We adopt a meta-learning approach, where the agent learns a policy for conducting experiments via causal interventions, in order to support a subsequent task which rewards making accurate causal inferences.We also found the agent could make sophisticated counterfactual predictions, as well as learn to draw causal inferences from purely observational data. Though powerful formalisms for causal reasoning have been developed, applying them in real-world domains can be difficult because fitting to large amounts of high dimensional data often requires making idealized assumptions. Our results suggest that causal reasoning in complex settings may benefit from powerful learning-based approaches. More generally, this work may offer new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform—and interpret—experiments.
Example Output: meta-learn a learning algorithm capable of causal reasoning

Example Input: Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable. In this work, we study these networks in the context of robot motion planning, with a focus on applications to planetary rovers. The key challenging task in learning-based motion planning is to learn a transformation from terrain observations to a suitable navigation reward function. In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN). SVIN is designed to produce more effective training gradients through the value iteration network. It relies on a soft policy model, where the policy is represented with a probability distribution over all possible actions, rather than a deterministic policy that returns only the best action. We demonstrate the effectiveness of the proposed method in robot motion planning scenarios. In particular, we study the application of SVIN to very challenging problems in planetary rover navigation and present early training results on data gathered by the  Curiosity rover that is currently operating on Mars.
Example Output: We propose an improvement to value iteration networks, with applications to planetary rover path planning.

Example Input: Emphasis effects – visual changes that make certain elements more
 prominent – are commonly used in information visualization to draw
 the user’s attention or to indicate importance. Although theoretical
 frameworks of emphasis exist (that link visually diverse emphasis
 effects through the idea of visual prominence compared to background
 elements), most metrics for predicting how emphasis effects
 will be perceived by users come from abstract models of human
 vision which may not apply to visualization design. In particular,
 it is difficult for designers to know, when designing a visualization,
 how different emphasis effects will compare and what level of one
 effect is equivalent to what level of another. To address this gap,
 we carried out two studies that provide empirical evidence about
 how users perceive different emphasis effects, using three visual
 variables (colour, size, and blur/focus) and eight strength levels.
 Results from gaze tracking, mouse clicks, and subjective responses
 show that there are significant differences between visual variables
 and between levels, and allow us to develop an initial understanding
 of perceptual equivalence. We developed a model from the data in
 our first study, and used it to predict the results in the second; the
 model was accurate, with high correlations between predictions and
 real values. Our studies and empirical models provide valuable new
 information for designers who want to understand and control how
 emphasis effects will be perceived by users.
Example Output:
Our studies and empirical models provide valuable new information for designers who want to understand and control how emphasis effects will be perceived by users