31,066 research outputs found
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown
distinct advantages, e.g., solving memory-dependent tasks and meta-learning.
However, little effort has been spent on improving RNN architectures and on
understanding the underlying neural mechanisms for performance gain. In this
paper, we propose a novel, multiple-timescale, stochastic RNN for RL. Empirical
results show that the network can autonomously learn to abstract sub-goals and
can self-develop an action hierarchy using internal dynamics in a challenging
continuous control task. Furthermore, we show that the self-developed
compositionality of the network enhances faster re-learning when adapting to a
new task that is a re-composition of previously learned sub-goals, than when
starting from scratch. We also found that improved performance can be achieved
when neural activities are subject to stochastic rather than deterministic
dynamics
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
Image cropping aims at improving the aesthetic quality of images by adjusting
their composition. Most weakly supervised cropping methods (without bounding
box supervision) rely on the sliding window mechanism. The sliding window
mechanism requires fixed aspect ratios and limits the cropping region with
arbitrary size. Moreover, the sliding window method usually produces tens of
thousands of windows on the input image which is very time-consuming. Motivated
by these challenges, we firstly formulate the aesthetic image cropping as a
sequential decision-making process and propose a weakly supervised Aesthetics
Aware Reinforcement Learning (A2-RL) framework to address this problem.
Particularly, the proposed method develops an aesthetics aware reward function
which especially benefits image cropping. Similar to human's decision making,
we use a comprehensive state representation including both the current
observation and the historical experience. We train the agent using the
actor-critic architecture in an end-to-end manner. The agent is evaluated on
several popular unseen cropping datasets. Experiment results show that our
method achieves the state-of-the-art performance with much fewer candidate
windows and much less time compared with previous weakly supervised methods.Comment: Accepted by CVPR 201
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