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:
Generative Adversarial Networks (GANs), when trained on large datasets with diverse modes, are known to produce conflated images which do not distinctly belong to any of the modes. We hypothesize that this problem occurs due to the interaction between two facts: (1) For datasets with large variety, it is likely that the modes lie on separate manifolds. (2) The generator (G) is formulated as a continuous function, and the input noise is derived from a connected set, due to which G's output is a connected set. If G covers all modes, then there must be some portion of G's output which connects them. This corresponds to undesirable, conflated images. We develop theoretical arguments to support these intuitions. We propose a novel method to break the second assumption via learnable discontinuities in the latent noise space. Equivalently, it can be viewed as training several generators, thus creating discontinuities in the G function. We also augment the GAN formulation with a classifier C that predicts which noise partition/generator produced the output images, encouraging diversity between each partition/generator. We experiment on MNIST, celebA, STL-10, and a difficult dataset with clearly distinct modes, and show that the noise partitions correspond to different modes of the data distribution, and produce images of superior quality.
answer:
We introduce theory to explain the failure of GANs on complex datasets and propose a solution to fix it.


question:
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples.   As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed.
 In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.
answer:
We propose AGILE, a framework for training agents to perform instructions from examples of respective goal-states.


question:
Reinforcement learning (RL) is a powerful technique to train an agent to perform a task.   However, an agent that is trained using RL is only capable of achieving the single task that is specified via its reward function.    Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations.   Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing in its environment.   We use a generator network to propose tasks for the agent to try to achieve, each task being specified as reaching a certain parametrized subset of the state-space.   The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent.   Our method thus automatically produces a curriculum of tasks for the agent to learn.   We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment (Videos and code available at: https://sites.google.com/view/goalgeneration4rl). Our method can also learn to achieve tasks with sparse rewards, which pose significant challenges for traditional RL methods.
answer:

We efficiently solve multi-task problems with an automatic curriculum generation algorithm based on a generative model that tracks the learning agent's performance.