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.

Input: Consider Input: Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments. We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations. We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information. In addition, we propose txt2, a model that captures three-way interactions between the goal, document, and observations. On RTFM, txt2 generalises to new environments with dynamics not seen during training via reading. Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM. Through curriculum learning, txt2 produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.

Output: We show language understanding via reading is promising way to learn policies that generalise to new environments.

Input: Consider Input: We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset. SOSELETO is based on the following simple intuition: some source examples are more informative than others for the target problem. To capture this intuition, source samples are each given weights; these weights are solved for jointly with the source and target classification problems via a bilevel optimization scheme. The target therefore gets to choose the source samples which are most informative for its own classification task. Furthermore, the bilevel nature of the optimization acts as a kind of regularization on the target, mitigating overfitting. SOSELETO may be applied to both classic transfer learning, as well as the problem of training on datasets with noisy labels; we show state of the art results on both of these problems.

Output: Learning with limited training data by exploiting "helpful" instances from a rich data source.

Input: Consider Input: Applying reinforcement learning (RL) to real-world problems will require reasoning about action-reward correlation over long time horizons. Hierarchical reinforcement learning (HRL) methods handle this by dividing the task into hierarchies, often with hand-tuned network structure or pre-defined subgoals. We propose a novel HRL framework TAIC, which learns the temporal abstraction from past experience or expert demonstrations without task-specific knowledge. We formulate the temporal abstraction problem as learning latent representations of action sequences and present a novel approach of regularizing the latent space by adding information-theoretic constraints. Specifically, we maximize the mutual information between the latent variables and the state changes.
A visualization of the latent space demonstrates that our algorithm learns an effective abstraction of the long action sequences. The learned abstraction allows us to learn new tasks on higher level more efficiently. We convey a significant speedup in convergence over benchmark learning problems. These results demonstrate that learning temporal abstractions is an effective technique in increasing the convergence rate and sample efficiency of RL algorithms.
Output: We propose a novel HRL framework, in which we formulate the temporal abstraction problem as learning a latent representation of action sequence.