51,374 research outputs found
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning
In open-ended environments, autonomous learning agents must set their own
goals and build their own curriculum through an intrinsically motivated
exploration. They may consider a large diversity of goals, aiming to discover
what is controllable in their environments, and what is not. Because some goals
might prove easy and some impossible, agents must actively select which goal to
practice at any moment, to maximize their overall mastery on the set of
learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a
modular Universal Value Function Approximator with hindsight learning to
achieve a diversity of goals of different kinds within a unique policy and 2)
an automated curriculum learning mechanism that biases the attention of the
agent towards goals maximizing the absolute learning progress. Agents focus
sequentially on goals of increasing complexity, and focus back on goals that
are being forgotten. Experiments conducted in a new modular-goal robotic
environment show the resulting developmental self-organization of a learning
curriculum, and demonstrate properties of robustness to distracting goals,
forgetting and changes in body properties.Comment: Accepted at ICML 201
Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces
We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence classification tasks with disparate label spaces. We outperform
strong single and multi-task baselines and achieve a new state-of-the-art for
topic-based sentiment analysis.Comment: To appear at NAACL 2018 (long
TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Experts
Learning discriminative task-specific features simultaneously for multiple
distinct tasks is a fundamental problem in multi-task learning. Recent
state-of-the-art models consider directly decoding task-specific features from
one shared task-generic feature (e.g., feature from a backbone layer), and
utilize carefully designed decoders to produce multi-task features. However, as
the input feature is fully shared and each task decoder also shares decoding
parameters for different input samples, it leads to a static feature decoding
process, producing less discriminative task-specific representations. To tackle
this limitation, we propose TaskExpert, a novel multi-task mixture-of-experts
model that enables learning multiple representative task-generic feature spaces
and decoding task-specific features in a dynamic manner. Specifically,
TaskExpert introduces a set of expert networks to decompose the backbone
feature into several representative task-generic features. Then, the
task-specific features are decoded by using dynamic task-specific gating
networks operating on the decomposed task-generic features. Furthermore, to
establish long-range modeling of the task-specific representations from
different layers of TaskExpert, we design a multi-task feature memory that
updates at each layer and acts as an additional feature expert for dynamic
task-specific feature decoding. Extensive experiments demonstrate that our
TaskExpert clearly outperforms previous best-performing methods on all 9
metrics of two competitive multi-task learning benchmarks for visual scene
understanding (i.e., PASCAL-Context and NYUD-v2). Codes and models will be made
publicly available at https://github.com/prismformore/Multi-Task-TransformerComment: Accepted by ICCV 202
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